86 results on '"Ramachandra, Raghavendra"'
Search Results
2. A uniform representation model for OCT-based fingerprint presentation attack detection and reconstruction
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Zhang, Wentian, Liu, Haozhe, Liu, Feng, and Ramachandra, Raghavendra
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- 2024
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3. A machine learning approach for assessing the compressive strength of cementitious composites reinforced by graphene derivatives
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Montazerian, Arman, Baghban, Mohammad Hajmohammadian, Ramachandra, Raghavendra, and Goutianos, Stergios
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- 2023
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4. Facilitating free travel in the Schengen area—A position paper by the European Association for Biometrics
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Christoph Busch, Farzin Deravi, Dinusha Frings, Els Kindt, Ralph Lessmann, Alexander Nouak, Jean Salomon, Mateus Achcar, Fernando Alonso‐Fernandez, Daniel Bachenheimer, David Bethell, Josef Bigun, Matthew Brawley, Guido Brockmann, Enrique Cabello, Patrizio Campisi, Aleksandrs Cepilovs, Miles Clee, Mickey Cohen, Christian Croll, Andrzej Czyżewski, Bernadette Dorizzi, Martin Drahansky, Pawel Drozdowski, Catherine Fankhauser, Julian Fierrez, Marta Gomez‐Barrero, Georg Hasse, Richard Guest, Ekaterina Komleva, Sebastien Marcel, Gian Luca Marcialis, Laurent Mercier, Emilio Mordini, Stefance Mouille, Pavlina Navratilova, Javier Ortega‐Garcia, Dijana Petrovska, Norman Poh, Istvan Racz, Ramachandra Raghavendra, Christian Rathgeb, Christophe Remillet, Uwe Seidel, Luuk Spreeuwers, Brage Strand, Sirra Toivonen, and Andreas Uhl
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biometrics (access control) ,biometric template protection ,biometric applications ,computer vision ,data privacy ,image analysis for biometrics ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract Due to migration, terror‐threats and the viral pandemic, various EU member states have re‐established internal border control or even closed their borders. European Association for Biometrics (EAB), a non‐profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re‐establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re‐establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade‐off with regards to open borders while maintaining a high‐level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions.
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- 2023
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5. Fingerphoto morphing attack generation using texture descriptors based landmarks
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Li, Hailin and Ramachandra, Raghavendra
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- 2024
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6. Compact and progressive network for enhanced single image super-resolution—ComPrESRNet
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Chudasama, Vishal, Upla, Kishor, Raja, Kiran, Ramachandra, Raghavendra, and Busch, Christoph
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- 2022
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7. Towards generalized morphing attack detection by learning residuals
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Raja, Kiran, Gupta, Gourav, Venkatesh, Sushma, Ramachandra, Raghavendra, and Busch, Christoph
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- 2022
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8. Multimodality for Reliable Single Image Based Face Morphing Attack Detection
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Ramachandra Raghavendra and Guoqiang Li
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Biometrics ,attacks ,face biometrics ,morphing attacks ,multimodal modality ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Face morphing attacks have demonstrated a high vulnerability on human observers and commercial off-the-shelf Face Recognition Systems (FRS), especially in the border control scenario. Therefore, detecting face morphing attacks is paramount to achieving a reliable and secure border control operation. This work presents a novel framework for the Single image-based Morphing Attack Detection (S-MAD) based on the multimodal regions such as eyes, nose, and mouth. Each of these regions is processed using colour scale-space representation on which two different types of features are extracted using Binarised Statistical Image Features (BSIF) and Local Binary Features (LBP) techniques. These features are then fed to the classifiers such as Probabilistic Collaborative Representation Classifier (P-CRC) and Spectral Regression Kernel Discriminant Analysis (SRKDA). Their decisions are combined at score level to make the final decision. Extensive experiments are carried out on three different face morphing datasets to benchmark the performance of the proposed method with the existing methods. Further, the proposed method is benchmarked on the Bologna Online Evaluation Platform (BOEP). Obtained results demonstrate the improved performance of the proposed method over existing state-of-the-art methods.
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- 2022
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9. Iris and periocular biometrics for head mounted displays: Segmentation, recognition, and synthetic data generation
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Boutros, Fadi, Damer, Naser, Raja, Kiran, Ramachandra, Raghavendra, Kirchbuchner, Florian, and Kuijper, Arjan
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- 2020
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10. Collaborative representation of blur invariant deep sparse features for periocular recognition from smartphones
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Raja, Kiran, Ramachandra, Raghavendra, and Busch, Christoph
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- 2020
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11. A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches.
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Arıcan, Tuğçe, Veldhuis, Raymond, Spreeuwers, Luuk, Bergeron, Loïc, Busch, Christoph, Jalilian, Ehsaneddin, Kauba, Christof, Kirchgasser, Simon, Marcel, Sébastien, Prommegger, Bernhard, Raja, Kiran, Ramachandra, Raghavendra, and Uhl, Andreas
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CONVOLUTIONAL neural networks ,DEEP learning ,FINGERS ,VEINS ,HUMAN fingerprints - Abstract
Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Ethnobotanical study of traditional herbal plants used by local people of Seshachalam Biosphere Reserve in Eastern Ghats, India
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Reddy Araveeti Madhusudhana, Babu Madha Venkata Suresh, and Rao Ramachandra Raghavendra
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ethnomedicine ,Yanadis ,Seshachalam Biosphere Reserve ,Eastern Ghats ,herbal plants ,drug development ,Plant culture ,SB1-1110 - Abstract
Introduction: Ethnobotany is the study of medicinal plants used by local people, with particular importance of old-styled tribal beliefs and information. Ethnobotanical studies focus on ethnic knowledge of Adivasi people and development of data bases on ethnic knowledge but also focuses on preservation and regeneration of traditional beliefs and maintenance of traditional knowledge.
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- 2019
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13. Multi-biometric template protection based on bloom filters
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Gomez-Barrero, Marta, Rathgeb, Christian, Li, Guoqiang, Ramachandra, Raghavendra, Galbally, Javier, and Busch, Christoph
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- 2018
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14. Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?
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Li, Hailin and Ramachandra, Raghavendra
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
The rapid evolution of high-end smartphones with advanced high-resolution cameras has resulted in contactless capture of fingerprint biometrics that are more reliable and suitable for verification. Similar to other biometric systems, contactless fingerprint-verification systems are vulnerable to presentation attacks. In this paper, we present a comparative study on the generalizability of seven different pre-trained Convolutional Neural Networks (CNN) and a Vision Transformer (ViT) to reliably detect presentation attacks. Extensive experiments were carried out on publicly available smartphone-based presentation attack datasets using four different Presentation Attack Instruments (PAI). The detection performance of the eighth deep feature technique was evaluated using the leave-one-out protocol to benchmark the generalization performance for unseen PAI. The obtained results indicated the best generalization performance with the ResNet50 CNN., Preprint paper accepted by First Workshop on Contactless Hand Biometrics and Gesture Recognition (CHBGR-2023)
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- 2023
15. Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network
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Ramachandra, Raghavendra, Venkatesh, Sushma, Li, Guoqiang, and Raja, Kiran
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Face Recognition System (FRS) are shown to be vulnerable to morphed images of newborns. Detecting morphing attacks stemming from face images of newborn is important to avoid unwanted consequences, both for security and society. In this paper, we present a new reference-based/Differential Morphing Attack Detection (MAD) method to detect newborn morphing images using Wavelet Scattering Network (WSN). We propose a two-layer WSN with 250 $\times$ 250 pixels and six rotations of wavelets per layer, resulting in 577 paths. The proposed approach is validated on a dataset of 852 bona fide images and 2460 morphing images constructed using face images of 42 unique newborns. The obtained results indicate a gain of over 10\% in detection accuracy over other existing D-MAD techniques., accepted in 5th International Conference on Bio-engineering for Smart Technologies (BIO-SMART 2023)
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- 2023
16. SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution
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Patel, Dhruv, Jain, Abhinav, Bawkar, Simran, Khorasiya, Manav, Prajapati, Kalpesh, Upla, Kishor, Raja, Kiran, Ramachandra, Raghavendra, and Busch, Christoph
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Image and Video Processing (eess.IV) ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Many applications such as forensics, surveillance, satellite imaging, medical imaging, etc., demand High-Resolution (HR) images. However, obtaining an HR image is not always possible due to the limitations of optical sensors and their costs. An alternative solution called Single Image Super-Resolution (SISR) is a software-driven approach that aims to take a Low-Resolution (LR) image and obtain the HR image. Most supervised SISR solutions use ground truth HR image as a target and do not include the information provided in the LR image, which could be valuable. In this work, we introduce Triplet Loss-based Generative Adversarial Network hereafter referred as SRTGAN for Image Super-Resolution problem on real-world degradation. We introduce a new triplet-based adversarial loss function that exploits the information provided in the LR image by using it as a negative sample. Allowing the patch-based discriminator with access to both HR and LR images optimizes to better differentiate between HR and LR images; hence, improving the adversary. Further, we propose to fuse the adversarial loss, content loss, perceptual loss, and quality loss to obtain Super-Resolution (SR) image with high perceptual fidelity. We validate the superior performance of the proposed method over the other existing methods on the RealSR dataset in terms of quantitative and qualitative metrics., Affiliated with the Sardar Vallabhbhai National Institute of Technology (SVNIT), India and Norwegian University of Science and Technology (NTNU), Norway. Presented at the 7th International Conference on Computer Vision and Image Processing (CVIP) 2022
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- 2022
17. A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection
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Lokesh Nandanwar, Palaiahnakote Shivakumara, Hamid A. Jalab, Rabha W. Ibrahim, Ramachandra Raghavendra, Umapada Pal, Tong Lu, and Michael Blumenstein
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Artificial Intelligence ,Computer Networks and Communications ,Artificial Intelligence & Image Processing ,Software ,Computer Science Applications - Abstract
Detecting forged handwriting is important in a wide variety of machine learning applications, and it is challenging when the input images are degraded with noise and blur. This article presents a new model based on conformable moments (CMs) and deep ensemble neural networks (DENNs) for forged handwriting detection in noisy and blurry environments. Since CMs involve fractional calculus with the ability to model nonlinearities and geometrical moments as well as preserving spatial relationships between pixels, fine details in images are preserved. This motivates us to introduce a DENN classifier, which integrates stenographic kernels and spatial features to classify input images as normal (original, clean images), altered (handwriting changed through copy-paste and insertion operations), noisy (added noise to original image), blurred (added blur to original image), altered-noise (noise is added to the altered image), and altered-blurred (blur is added to the altered image). To evaluate our model, we use a newly introduced dataset, which comprises handwritten words altered at the character level, as well as several standard datasets, namely ACPR 2019, ICPR 2018-FDC, and the IMEI dataset. The first two of these datasets include handwriting samples that are altered at the character and word levels, and the third dataset comprises forged International Mobile Equipment Identity (IMEI) numbers. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of classification rate.
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- 2022
18. Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data
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Grimmer, Marcel, Zhang, Haoyu, Ramachandra, Raghavendra, Raja, Kiran, and Busch, Christoph
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FOS: Computer and information sciences ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
The vast progress in synthetic image synthesis enables the generation of facial images in high resolution and photorealism. In biometric applications, the main motivation for using synthetic data is to solve the shortage of publicly-available biometric data while reducing privacy risks when processing such sensitive information. These advantages are exploited in this work by simulating human face ageing with recent face age modification algorithms to generate mated samples, thereby studying the impact of ageing on the performance of an open-source biometric recognition system. Further, a real dataset is used to evaluate the effects of short-term ageing, comparing the biometric performance to the synthetic domain. The main findings indicate that short-term ageing in the range of 1-5 years has only minor effects on the general recognition performance. However, the correct verification of mated faces with long-term age differences beyond 20 years poses still a significant challenge and requires further investigation.
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- 2022
19. How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting
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Nag, Sauradip, Shah, Nisarg, Qi, Anran, and Ramachandra, Raghavendra
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Computational Geometry (cs.CG) ,FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Computational Geometry - Abstract
In this paper we present a novel self-supervised method to anticipate the depth estimate for a future, unobserved real-world urban scene. This work is the first to explore self-supervised learning for estimation of monocular depth of future unobserved frames of a video. Existing works rely on a large number of annotated samples to generate the probabilistic prediction of depth for unseen frames. However, this makes it unrealistic due to its requirement for large amount of annotated depth samples of video. In addition, the probabilistic nature of the case, where one past can have multiple future outcomes often leads to incorrect depth estimates. Unlike previous methods, we model the depth estimation of the unobserved frame as a view-synthesis problem, which treats the depth estimate of the unseen video frame as an auxiliary task while synthesizing back the views using learned pose. This approach is not only cost effective - we do not use any ground truth depth for training (hence practical) but also deterministic (a sequence of past frames map to an immediate future). To address this task we first develop a novel depth forecasting network DeFNet which estimates depth of unobserved future by forecasting latent features. Second, we develop a channel-attention based pose estimation network that estimates the pose of the unobserved frame. Using this learned pose, estimated depth map is reconstructed back into the image domain, thus forming a self-supervised solution. Our proposed approach shows significant improvements in Abs Rel metric compared to state-of-the-art alternatives on both short and mid-term forecasting setting, benchmarked on KITTI and Cityscapes. Code is available at https://github.com/sauradip/depthForecasting, Accepted in ML4AD Workshop, NeurIPS 2021
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- 2022
20. 3D Face Morphing Attacks: Generation, Vulnerability and Detection
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Singh, Jag Mohan and Ramachandra, Raghavendra
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
Face Recognition systems (FRS) have been found vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction towards generating face morphing attacks in 3D. To this extent, we have introduced a novel approach based on blending the 3D face point clouds corresponding to the contributory data subjects. The proposed method will generate the 3D face morphing by projecting the input 3D face point clouds to depth-maps \& 2D color images followed by the image blending and wrapping operations performed independently on the color images and depth maps. We then back-project the 2D morphing color-map and the depth-map to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in the holes due to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments are carried out on the newly generated 3D face dataset comprised of 675 3D scans corresponding to 41 unique data subjects. Experiments are performed to benchmark the vulnerability of automatic 2D and 3D FRS and human observer analysis. We also present the quantitative assessment of the quality of the generated 3D face morphing models using eight different quality metrics. Finally, we have proposed three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of the 3D MAD algorithms., The paper is currently under review at IEEE Transactions on Biometrics, Behavior and Identity Science
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- 2022
21. Explainable Visualization for Morphing Attack Detection
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Myhrvold, Henning, Zhang, Haoyu, Tapia, Juan, Ramachandra, Raghavendra, and Busch, Christoph Günther
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Detecting morphed face images has become critical for maintaining trust in automated facial biometric verification systems. It is well demonstrated that better biometric performance of the Face Recognition System (FRS) results in higher vulnerability to face morphing attacks. Morphing can be understood as a technique to combine two or more look-alike facial images corresponding to the attacker and an accomplice, who could apply for a valid passport by exploiting the accomplice’s identity. Morphing Attack Detection (MAD), with the help of Convolutional Neural Networks (CNN), has demonstrated good performance in detecting morphed images. However, they lack transparency, and it is unclear how they differentiate between bona fide and morphed facial images. As a result, this phenomenon needs careful consideration for safety and security-related applications. This paper will explore Layer-wise Relevance Propagation (LRP) to determine the most relevant features. We fine-tune a VGG pre-trained network for face morphing attack detection and LRP is then used to investigate the decision-making processes to understand what input pixels take part in the attack detection. This paper shows that CNN considers only a small part of the image, usually around the eyes, nose, and mouth.
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- 2022
22. A Knowledge Enforcement Network-Based Approach for Classifying a Photographer's Images.
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Shivakumara, Palaiahnakote, Chowdhury, Pinaki Nath, Pal, Umapada, Doermann, David, Ramachandra, Raghavendra, Lu, Tong, and Blumenstein, Michael
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PHOTOGRAPHERS ,DEEP learning ,IMAGE processing ,PHOTOGRAPHS ,LEARNING ability ,SOCIAL media ,DIGITAL cameras - Abstract
Classification of photos captured by different photographers is an important and challenging problem in knowledge-based and image processing. Monitoring and authenticating images uploaded on social media are essential, and verifying the source is one key piece of evidence. We present a novel framework for classifying photos of different photographers based on the combination of local features and deep learning models. The proposed work uses focused and defocused information in the input images to extract contextual information. The model estimates the weighted gradient and calculates entropy to strengthen context features. The focused and defocused information is fused to estimate cross-covariance and define a linear relationship between them. This relationship results in a feature matrix fed to Knowledge Enforcement Network (KEN) for obtaining representative features. Due to the strong discriminative ability of deep learning models, we employ the lightweight and accurate MobileNetV2. The output of KEN and MobileNetV2 is sent to a classifier for photographer classification. Experimental results of the proposed model on our dataset of 46 photographer classes (46234 images) and publicly available datasets of 41 photographer classes (218303 images) show that the method outperforms the existing techniques by 5%–10% on average. The dataset created for the experimental purpose will be made available upon publication. [ABSTRACT FROM AUTHOR]
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- 2022
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23. FRT-PAD: Effective Presentation Attack Detection Driven by Face Related Task
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Zhang, Wentian, Liu, Haozhe, Liu, Feng, Ramachandra, Raghavendra, and Busch, Christoph
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs). However, in a real scenario, Presentation Attacks (PAs) are various and it is hard to predict the Presentation Attack Instrument (PAI) species that will be used by the attacker. Existing PAD methods are highly dependent on the limited training set and cannot generalize well to unknown PAI species. Unlike this specific PAD task, other face related tasks trained by huge amount of real faces (e.g. face recognition and attribute editing) can be effectively adopted into different application scenarios. Inspired by this, we propose to trade position of PAD and face related work in a face system and apply the free acquired prior knowledge from face related tasks to solve face PAD, so as to improve the generalization ability in detecting PAs. The proposed method, first introduces task specific features from other face related task, then, we design a Cross-Modal Adapter using a Graph Attention Network (GAT) to re-map such features to adapt to PAD task. Finally, face PAD is achieved by using the hierarchical features from a CNN-based PA detector and the re-mapped features. The experimental results show that the proposed method can achieve significant improvements in the complicated and hybrid datasets, when compared with the state-of-the-art methods. In particular, when training on the datasets OULU-NPU, CASIA-FASD, and Idiap Replay-Attack, we obtain HTER (Half Total Error Rate) of 5.48% for the testing dataset MSU-MFSD, outperforming the baseline by 7.39%., Accepted by ECCV 2022
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- 2021
24. DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection
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Jaswal, Gaurav, Verma, Aman, Roy, Sumantra Dutta, and Ramachandra, Raghavendra
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computer Science - Computer Vision and Pattern Recognition - Abstract
An iris presentation attack detection (IPAD) is essential for securing personal identity is widely used iris recognition systems. However, the existing IPAD algorithms do not generalize well to unseen and cross-domain scenarios because of capture in unconstrained environments and high visual correlation amongst bonafide and attack samples. These similarities in intricate textural and morphological patterns of iris ocular images contribute further to performance degradation. To alleviate these shortcomings, this paper proposes DFCANet: Dense Feature Calibration and Attention Guided Network which calibrates the locally spread iris patterns with the globally located ones. Uplifting advantages from feature calibration convolution and residual learning, DFCANet generates domain-specific iris feature representations. Since some channels in the calibrated feature maps contain more prominent information, we capitalize discriminative feature learning across the channels through the channel attention mechanism. In order to intensify the challenge for our proposed model, we make DFCANet operate over nonsegmented and non-normalized ocular iris images. Extensive experimentation conducted over challenging cross-domain and intra-domain scenarios highlights consistent outperforming results. Compared to state-of-the-art methods, DFCANet achieves significant gains in performance for the benchmark IIITD CLI, IIIT CSD and NDCLD13 databases respectively. Further, a novel incremental learning-based methodology has been introduced so as to overcome disentangled iris-data characteristics and data scarcity. This paper also pursues the challenging scenario that considers soft-lens under the attack category with evaluation performed under various cross-domain protocols. The code will be made publicly available.
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- 2021
25. ReGenMorph: Visibly Realistic GAN Generated Face Morphing Attacks by Attack Re-generation
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Damer, Naser, Raja, Kiran, S����milch, Marius, Venkatesh, Sushma, Boutros, Fadi, Fang, Meiling, Kirchbuchner, Florian, Ramachandra, Raghavendra, and Kuijper, Arjan
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks. While creating a morphed face detector (MFD), training on all possible attack types is essential to achieve good detection performance. Therefore, investigating new methods of creating morphing attacks drives the generalizability of MADs. Creating morphing attacks was performed on the image level, by landmark interpolation, or on the latent-space level, by manipulating latent vectors in a generative adversarial network. The earlier results in varying blending artifacts and the latter results in synthetic-like striping artifacts. This work presents the novel morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using a GAN-based generation, as well as, eliminate the manipulation in the latent space, resulting in visibly realistic morphed images compared to previous works. The generated ReGenMorph appearance is compared to recent morphing approaches and evaluated for face recognition vulnerability and attack detectability, whether as known or unknown attacks., Accepted at the 16th International Symposium on Visual Computing (ISVC 2021)
- Published
- 2021
26. Collaborative representation of convolutional neural network features to detect artificial ripening of banana using multispectral imaging.
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Vetrekar, Narayan, Prabhu, Anish K., Naik, Aparajita, Ramachandra, Raghavendra, Raja, Kiran B., Desai, Adavi R., and Gad, Rajendra S.
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MULTISPECTRAL imaging ,CONVOLUTIONAL neural networks ,BANANAS ,ELECTROMAGNETIC spectrum ,PRODUCE markets ,CALCIUM carbide - Abstract
With the increasing demand for fruits in the consumer market and to fulfill the needs of consumers, fruits are deliberately resorted to ripen artificially using industrial‐grade Calcium Carbide (CaC2) which has carcinogenic properties. The preferences to employ imaging sensing technology across the electromagnetic spectrum have gained noteworthy attention in recent times. This work presents the study to distinguish between natural and artificially ripened bananas using a multispectral imaging approach. Specifically, we carry out the analysis using multispectral images collected for natural and artificially ripened bananas in eight narrow spectrum bands spanning from visible (VIS) to near‐infrared (NIR) wavelength. Further, to present the contribution of each individual spectral band, we propose a scheme that extracts the relevant features from "convolution 5 (conv5)" and "fully connected 6 (fc6)" layers of convolutional neural network (CNN) based on AlexNet architecture and processes these features independently with collaborative representation classifier (CRC) in a robust manner. Essentially, we demonstrate the experimental classification accuracy based on multispectral images comprised of 5760 sample images using 10‐fold cross‐validation. Based on the proposed scheme, the highest average classification accuracy of about 88.82 ± 1.65% is obtained using our proposed approach presenting the significance of our work. Practical applications: The practical application of our proposed approach is to distinguish between natural and artificially ripened banana fruits in a market chain. Further, the approach is non‐invasive and non‐destructive based on multispectral imaging which has the potential to carry out real‐time quality analysis of bananas, especially to detect adulteration. [ABSTRACT FROM AUTHOR]
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- 2022
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27. Pixel‐wise supervision for presentation attack detection on identity document cards.
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Mudgalgundurao, Raghavendra, Schuch, Patrick, Raja, Kiran, Ramachandra, Raghavendra, and Damer, Naser
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IDENTIFICATION cards ,DEEP learning ,ERROR rates ,BORDER security ,SUPERVISION ,CLASSIFICATION - Abstract
Identity documents (or IDs) play an important role in verifying the identity of a person with wide applications in banks, travel, video‐identification services and border controls. Replay or photocopied ID cards can be misused to pass ID control in unsupervised scenarios if the liveness of a person is not checked. To detect such presentation attacks on ID card verification process when presented virtually is a critical step for the biometric systems to assure authenticity. In this paper, a pixel‐wise supervision on DenseNet is proposed to detect presentation attacks of the printed and digitally replayed attacks. The authors motivate the approach to use pixel‐wise supervision to leverage minute cues on various artefacts such as moiré patterns and artefacts left by the printers. The baseline benchmark is presented using different handcrafted and deep learning models on a newly constructed in‐house database obtained from an operational system consisting of 886 users with 433 bona fide, 67 print and 366 display attacks. It is demonstrated that the proposed approach achieves better performance compared to handcrafted features and Deep Models with an Equal Error Rate of 2.22% and Bona fide Presentation Classification Error Rate (BPCER) of 1.83% and 1.67% at Attack Presentation Classification Error Rate of 5% and 10%. [ABSTRACT FROM AUTHOR]
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- 2022
- Full Text
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28. Ethnobotanical study of traditional herbal plants used by local people of Seshachalam Biosphere Reserve in Eastern Ghats, India
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Araveeti Madhusudhana Reddy, Madha Venkata Suresh Babu, and Ramachandra Raghavendra Rao
- Subjects
010405 organic chemistry ,Agroforestry ,Biosphere ,ethnomedicine ,lcsh:Plant culture ,01 natural sciences ,drug development ,0104 chemical sciences ,Eastern Ghats ,herbal plants ,010404 medicinal & biomolecular chemistry ,Geography ,Yanadis ,Ethnobotany ,lcsh:SB1-1110 ,Traditional knowledge ,Seshachalam Biosphere Reserve ,Medicinal plants ,Ethnomedicine - Abstract
Summary Introduction: Ethnobotany is the study of medicinal plants used by local people, with particular importance of old-styled tribal beliefs and information. Ethnobotanical studies focus on ethnic knowledge of Adivasi people and development of data bases on ethnic knowledge but also focuses on preservation and regeneration of traditional beliefs and maintenance of traditional knowledge. Objective: The aim of present study is to highlight the traditional actions of herbal plants used by inborn Yanadi community of Seshachalam Biosphere Reserve, Eastern Ghats of Andhra Pradesh, India. Methods: The ethnobotanical field survey was conducted according to the methods adopted by some authors. In-depth interviews, interactions were conducted with tribal physicians of Yanadi, Nakkala and Irula as well as other tribes practicing and experiencing the use of plant-based medicine. A normal inquiry form was used to gather the appropriate data on herbal plants and their usage of inborn people’s lifestyle. Extensive consultations among local people and detailed documentation of the usage of plants were carried out Results: A total of 266 medicinally used plant species belonging to 216 genera and 88 families were recognized with help of inborn herbal healers. The study also chronicled the mode of herbal arrangements, mode of the use of herbal plants in various disorders. The study exposed that native people of Seshachalam Biosphere Reserve have good medicinal information and also have preserved plant-based medicinal system of their ascendants used all their diseases. Most of medicinal plants are used in the treatment of indigestion, snake bite and skin diseases. The authors feel that this type of study certainly helps identify ethnic leads for drug development in future. Conclusions: The ethnobotanical investigation of Seshalam Biosphere area has revealed that the tribes possess good knowledge on plant-based medicine but as they are towards in advanced exposure to transformation, their information on traditional uses of plants is slowly getting eroded. The authors plead for intensive crosscultural studies involving all ethnic tribes in the country for prioritizing or short listing of ethnic leads for various disorders for ultimately developing global level drugs for human welfare and economy development.
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- 2019
29. Cross-lingual Speaker Verification: Evaluation On X-Vector Method
- Author
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Mandalapu, Hareesh, Møller Elbo, Thomas, Ramachandra, Raghavendra, and Busch, Christoph
- Abstract
Automatic Speaker Verification (ASV) systems accuracy is based on the spoken language used in training and enrolling speakers. Language dependency makes voice-based security systems less robust and generalizable to a wide range of applications. In this work, a study on language dependency of a speaker verification system and experiments are performed to benchmark the robustness of the x-vector based techniques to language dependency. Experiments are carried out on a smartphone multi-lingual dataset with 50 subjects containing utterances in four different languages captured in five sessions. We have used two world training datasets, one with only one language and one with multiple languages. Results show that performance is degraded when there is a language mismatch in enrolling and testing. Further, our experimental results indicate that the performance degradation depends on the language present in the word training data. "This is a post-peer-review, pre-copyedit version of an article. The final authenticated version is available online at: https://www.springerprofessional.de/en/cross-lingual-speaker-verification-evaluation-on-x-vector-method/18963582
- Published
- 2021
30. MIPGAN - Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN
- Author
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Zhang, Haoyu, Venkatesh, Sushma, Ramachandra, Raghavendra, Raja, Kiran, Damer, Naser, Busch, Christoph, and Publica
- Subjects
FOS: Computer and information sciences ,Research Line: Computer vision (CV) ,biometrics ,Computer Science - Cryptography and Security ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,deep learning ,Generative Adversarial Networks (GAN) ,CRISP ,manual ,Lead Topic: Smart City ,Research Line: Human computer interaction (HCI) ,Research Line: Machine Learning (ML) ,ATHENE ,Lead Topic: Visual Computing as a Service ,Morphing Attack ,Cryptography and Security (cs.CR) ,visualization ,face recognition - Abstract
Face morphing attacks target to circumvent Face Recognition Systems (FRS) by employing face images derived from multiple data subjects (e.g., accomplices and malicious actors). Morphed images can be verified against contributing data subjects with a reasonable success rate, given they have a high degree of facial resemblance. The success of morphing attacks is directly dependent on the quality of the generated morph images. We present a new approach for generating strong attacks extending our earlier framework for generating face morphs. We present a new approach using an Identity Prior Driven Generative Adversarial Network, which we refer to as MIPGAN (Morphing through Identity Prior driven GAN). The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution. We demonstrate the proposed approach's applicability to generate strong morphing attacks by evaluating its vulnerability against both commercial and deep learning based Face Recognition System (FRS) and demonstrate the success rate of attacks. Extensive experiments are carried out to assess the FRS's vulnerability against the proposed morphed face generation technique on three types of data such as digital images, re-digitized (printed and scanned) images, and compressed images after re-digitization from newly generated MIPGAN Face Morph Dataset. The obtained results demonstrate that the proposed approach of morph generation poses a high threat to FRS., Comment: Revised version. Submitted to IEEE T-BIOM 2020
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- 2021
31. A New Deep Wavefront Based Model for Text Localization in 3D Video.
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Nandanwar, Lokesh, Shivakumara, Palaiahnakote, Ramachandra, Raghavendra, Lu, Tong, Pal, Umapada, Antonacopoulos, Apostolos, and Lu, Yue
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LOCALIZATION (Mathematics) ,ELECTRONIC equipment ,VIDEO processing ,VIDEOS - Abstract
With the evolution of electronic devices, such as 3D cameras, addressing the challenges of text localization in 3D video (e.g., for indexing) is increasingly drawing the attention of the multimedia and video processing community. Existing methods focus on 2D video and their performance in the presence of the challenges in 3D video, such as shadow areas associated with text and irregularly sized and shaped text, degrades. This paper proposes the first approach that successfully addresses the challenges of 3D video in addition to those of 2D. It employs a number of innovations, among which, the first is the Generalized Gradient Vector Flow (GGVF) for dominant points detection. The second is the Wavefront concept for text candidate point detection from those dominant points. In addition, an Adaptive B-Spline Polygon Curve Network (ABS-Net) is proposed for accurate text localization in 3D videos by constructing tight fitting bounding polygons using text candidate points. Extensive experiments on custom (3D video) and standard datasets (2D video and scene text) show that the proposed method is practical and useful, and overall outperforms existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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- 2022
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32. On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application
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Boutros, Fadi, Damer, Naser, Raja, Kiran, Ramachandra, Raghavendra, Kirchbuchner, Florian, and Kuijper, Arjan
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computer Science - Computer Vision and Pattern Recognition ,Machine Learning (cs.LG) - Abstract
Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Mounted Displays (HMD) developed for such applications commonly contains internal cameras for gaze tracking purposes, we evaluate the suitability of such setup for verifying the users through iris recognition. In this work, we first evaluate a set of iris recognition algorithms suitable for HMD devices by investigating three well-established handcrafted feature extraction approaches, and to complement it, we also present the analysis using four deep learning models. While taking into consideration the minimalistic hardware requirements of stand-alone HMD, we employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Further, to account for non-ideal and non-collaborative capture of iris, we define a new iris quality metric that we termed as Iris Mask Ratio (IMR) to quantify the iris recognition performance. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD. Through the experiments on a publicly available OpenEDS dataset, we show that performance with EER = 5% can be achieved using deep learning methods in a general setting, along with high accuracy for continuous user authentication., Accepted at International Join Conference on Biometrics (IJCB 2020)
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- 2020
33. Recent advances in biometric technology for mobile devices
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Das, Abhijit, Galdi, Chiar, Han, Hu, Ramachandra, Raghavendra, Dugelay, Jean-Luc, Dantcheva, Antitza, Das, Abhijit, Galdi, Chiar, Han, Hu, Ramachandra, Raghavendra, Dugelay, Jean-Luc, and Dantcheva, Antitza
- Published
- 2018
34. Face Morphing Attack Generation & Detection: A Comprehensive Survey
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Venkatesh, Sushma, Ramachandra, Raghavendra, Raja, Kiran, and Busch, Christoph
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FOS: Computer and information sciences ,Computer Science - Computers and Society ,Computer Science - Cryptography and Security ,Computer Vision and Pattern Recognition (cs.CV) ,Computers and Society (cs.CY) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Cryptography and Security (cs.CR) - Abstract
The vulnerability of Face Recognition System (FRS) to various kind of attacks (both direct and in-direct attacks) and face morphing attacks has received a great interest from the biometric community. The goal of a morphing attack is to subvert the FRS at Automatic Border Control (ABC) gates by presenting the Electronic Machine Readable Travel Document (eMRTD) or e-passport that is obtained based on the morphed face image. Since the application process for the e-passport in the majority countries requires a passport photo to be presented by the applicant, a malicious actor and the accomplice can generate the morphed face image and to obtain the e-passport. An e-passport with a morphed face images can be used by both the malicious actor and the accomplice to cross the border as the morphed face image can be verified against both of them. This can result in a significant threat as a malicious actor can cross the border without revealing the track of his/her criminal background while the details of accomplice are recorded in the log of the access control system. This survey aims to present a systematic overview of the progress made in the area of face morphing in terms of both morph generation and morph detection. In this paper, we describe and illustrate various aspects of face morphing attacks, including different techniques for generating morphed face images but also the state-of-the-art regarding Morph Attack Detection (MAD) algorithms based on a stringent taxonomy and finally the availability of public databases, which allow to benchmark new MAD algorithms in a reproducible manner. The outcomes of competitions/benchmarking, vulnerability assessments and performance evaluation metrics are also provided in a comprehensive manner. Furthermore, we discuss the open challenges and potential future works that need to be addressed in this evolving field of biometrics.
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- 2020
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35. Detecting Finger-Vein Presentation Attacks Using 3D Shape & Diffuse Reflectance Decomposition
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Singh, Jag Mohan, Venkatesh, Sushma, Raja, Kiran B., Ramachandra, Raghavendra, and Busch, Christoph
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I.2.10 ,I.5.4 ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Computer Science - Computer Vision and Pattern Recognition ,I.4.9 ,Electrical Engineering and Systems Science - Image and Video Processing ,I.5.4, I.4.9, I.2.10 - Abstract
Despite the high biometric performance, finger-vein recognition systems are vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we present a new and robust approach for detecting presentation attacks on finger-vein biometric systems exploiting the 3D Shape (normal-map) and material properties (diffuse-map) of the finger. Observing the normal-map and diffuse-map exhibiting enhanced textural differences in comparison with the original finger-vein image, especially in the presence of varying illumination intensity, we propose to employ textural feature-descriptors on both of them independently. The features are subsequently used to compute a separating hyper-plane using Support Vector Machine (SVM) classifiers for the features computed from normal-maps and diffuse-maps independently. Given the scores from each classifier for normal-map and diffuse-map, we propose sum-rule based score level fusion to make detection of such presentation attack more robust. To this end, we construct a new database of finger-vein images acquired using a custom capture device with three inbuilt illuminations and validate the applicability of the proposed approach. The newly collected database consists of 936 images, which corresponds to 468 bona fide images and 468 artefact images. We establish the superiority of the proposed approach by benchmarking it with classical textural feature-descriptor applied directly on finger-vein images. The proposed approach outperforms the classical approaches by providing the Attack Presentation Classification Error Rate (APCER) & Bona fide Presentation Classification Error Rate (BPCER) of 0% compared to comparable traditional methods., Comment: This work was accepted in The 15th International Conference on SIGNAL IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS, 2019
- Published
- 2019
36. A New U-Net Based License Plate Enhancement Model in Night and Day Images
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Michael Blumenstein, Umapada Pal, Palaiahnakote Shivakumara, Pinaki Nath Chowdhury, Ramachandra Raghavendra, and Tong Lu
- Subjects
050210 logistics & transportation ,Pixel ,business.industry ,Computer science ,05 social sciences ,Process (computing) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Context (language use) ,02 engineering and technology ,Text detection ,Image enhancement ,Convolutional neural network ,0502 economics and business ,0202 electrical engineering, electronic engineering, information engineering ,Benchmark (computing) ,020201 artificial intelligence & image processing ,Computer vision ,Artificial Intelligence & Image Processing ,Artificial intelligence ,business ,License - Abstract
A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection.
- Published
- 2019
37. Vulnerabilities of Unattended Face Verification Systems to Facial Components-based Presentation Attacks: An Empirical Study.
- Author
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LE QIN, FEI PENG, MIN LONG, RAMACHANDRA, RAGHAVENDRA, and BUSCH, CHRISTOPH
- Subjects
COMMERCIALISM in schools ,DATABASES ,PARADIGM (Linguistics) ,GENERALIZATION ,INDUCTION (Logic) - Abstract
As face presentation attacks (PAs) are realistic threats for unattended face verification systems, face presentation attack detection (PAD) has been intensively investigated in past years, and the recent advances in face PAD have significantly reduced the success rate of such attacks. In this article, an empirical study on a novel and effective face impostor PA is made. In the proposed PA, a facial artifact is created by using the most vulnerable facial components, which are optimally selected based on the vulnerability analysis of different facial components to impostor PAs. An attacker can launch a face PA by presenting a facial artifact on his or her own real face. With a collected PA database containing various types of artifacts and presentation attack instruments (PAIs), the experimental results and analysis show that the proposed PA poses a more serious threat to face verification and PAD systems compared with the print, replay, and mask PAs. Moreover, the generalization ability of the proposed PA and the vulnerability analysis with regard to commercial systems are also investigated by evaluating unknown face verification and real-world PAD systems. It provides a new paradigm for the study of face PAs. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
38. A response to the European Data Protection Supervisor 'Misunderstandings in Biometrics' by the European Association for Biometrics.
- Author
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Busch, Christoph, Czajka, Adam, Deravi, Farzin, Drozdowski, Pawel, Gomez‐Barrero, Marta, Hasse, Georg, Henniger, Olaf, Kindt, Els, Kolberg, Jascha, Nouak, Alexander, Raja, Kiran, Ramachandra, Raghavendra, Rathgeb, Christian, Salomon, Jean, and Veldhuis, Raymond
- Subjects
BIOMETRIC identification ,DATA protection ,HUMAN facial recognition software ,DATA encryption - Abstract
The intention of this position paper is to comment on the joint European Data Protection Supervisor (EDPS)‐Agencia Española de Protección de Datos (aepd) publication '14 Misunderstandings with regard to Biometric Identification and Authentication' that was published in June 2020 and to provide additional input to help with the better understanding of the issues raised in that publication. In particular, it aims to highlight some important missing information in the aforementioned publication. It is hoped that this paper will help with any future revision of the EDPS‐aepd publication, such that it includes a full picture of the current state of the art in biometrics and the availability of standards and privacy enhancing techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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39. A New Foreground-Background based Method for Behavior-Oriented Social Media Image Classification.
- Author
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NANDANWAR, LOKESH, SHIVAKUMARA, PALAIAHNAKOTE, KRISHNANI, DIVYA, RAMACHANDRA, RAGHAVENDRA, TONG LU, PAL, UMAPADA, and KANKANHALLI, MOHAN
- Subjects
SOCIAL media ,FACIAL expression & emotions (Psychology) ,CLASSIFICATION ,FOLKSONOMIES ,PERSONALITY - Abstract
Due to various applications, research on personal traits using information on social media has become an important area. In this paper, a new method for the classification of behavior-oriented social images uploaded on various social media platforms is presented. The proposed method introduces a multimodality concept using skin of different parts of human body and background information, such as indoor and outdoor environments. For each image, the proposed method detects skin candidate components based on R, G, B color spaces and entropy features. The iterative mutual nearest neighbor approach is proposed to detect accurate skin candidate components, which result in foreground components. Next, the proposed method detects the remaining part (other than skin components) as background components based on structure tensor of R, G, B color spaces, and Maximally Stable Extremal Regions (MSER) concept in the wavelet domain.We then explore Hanman Transform for extracting context features from foreground and background components through clustering and fusion operation. These features are then fed to an SVM classifier for the classification of behavior-oriented images. Comprehensive experiments on 10-class datasets of Normal Behavior- Oriented Social media Image (NBSI) and Abnormal Behavior-Oriented Social media Image (ABSI) show that the proposed method is effective and outperforms the existing methods in terms of average classification rate. Also, the results on the benchmark dataset of five classes of personality traits and two classes of emotions of different facial expressions (FERPlus dataset) demonstrated the robustness of the proposed method over the existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
40. Anchored kernel hashing for cancelable template protection for cross-spectral periocular data
- Author
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Bylappa Raja, Kiran, Ramachandra, Raghavendra, and Busch, Christoph
- Abstract
Periocular characteristics is gaining prominence in biometric systems and surveillance systems that operate either in NIR spectrum or visible spectrum. While the ocular information can be well utilized, there exists a challenge to compare images from different spectra such as Near-Infra-Red (NIR) versus Visible spectrum (VIS). In addition, the ocular biometric templates from both NIR and VIS domain need to be protected after the extraction of features to avoid the leakage or linkability of biometric data. In this work, we explore a new approach based on anchored kernel hashing to obtain a cancelable biometric template that is both discriminative for recognition purposes while preserving privacy. The key benefit is that the proposed approach not only works for both NIR and the Visible spectrum, it can also be used with good accuracy for cross-spectral protected template comparison. Through the set of experiments using a cross-spectral periocular database, we demonstrate the performance with EER=1.39% and EER=1.61% for NIR and VIS protected templates respectively. We further present a set of cross-spectral template comparison by comparing the protected templates from one spectrum to another spectra to demonstrate the applicability of the proposed approach.
- Published
- 2019
41. Generation of Non-Deterministic Synthetic Face Datasets Guided by Identity Priors.
- Author
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Grimmer, Marcel, Haoyu Zhang, Ramachandra, Raghavendra, Raja, Kiran, and Busch, Christoph
- Subjects
FACE perception ,MULTIPLE correspondence analysis (Statistics) ,VECTORS (Calculus) ,BIOMETRIC identification ,DEEP learning - Abstract
Enabling highly secure applications (such as border crossing) with face recognition requires extensive biometric performance tests through large scale data. However, using real face images raises concerns about privacy as the laws do not allow the images to be used for other purposes than originally intended. Using representative and subsets of face data can also lead to unwanted demographic biases and cause an imbalance in datasets. One possible solution to overcome these issues is to replace real face images with synthetically generated samples. While generating synthetic images has benefited from recent advancements in computer vision, generating multiple samples of the same synthetic identity resembling real-world variations is still unaddressed, i.e., mated samples. This work proposes a non-deterministic method for generating mated face images by exploiting the well-structured latent space of StyleGAN. Mated samples are generated by manipulating latent vectors, and more precisely, we exploit Principal Component Analysis (PCA) to define semantically meaningful directions in the latent space and control the similarity between the original and the mated samples using a pre-trained face recognition system. We create a new dataset of synthetic face images (SymFace) consisting of 77,034 samples including 25,919 synthetic IDs. Through our analysis using well-established face image quality metrics, we demonstrate the difierences in the biometric quality of synthetic samples mimicking characteristics of real biometric data. The analysis and results thereof indicate the use of synthetic samples created using the proposed approach as a viable alternative to replacing real biometric data. [ABSTRACT FROM AUTHOR]
- Published
- 2021
42. Detection of artificially ripened banana using spectral signature from multi-spectral imaging.
- Author
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Vetrekar, Narayan, Ramachandra, Raghavendra, Raja, Kiran B., Gad, R. S., Naik, Aparajita, Prabhu, Anish, Shaligram, Arvind D., Gharpure, Damayanti C., Gaol, Ford Lumban, and Sharma, Niti Nipun
- Subjects
- *
MULTISPECTRAL imaging , *ARSENIC poisoning , *BANANAS , *BIG data , *INSPECTION & review , *FRUIT quality , *ANALYTICAL chemistry , *FRUIT ripening - Abstract
Ripening is a natural process of fruit maturation by which it attains desirable texture, aroma, colour and becomes more flavoursome. To meet the increasing needs of consumers, traders have resorted to artificial ripening of fruits. The ripening agent such as industrial-grade Calcium carbide (CaC2) degrades the overall quality of the fruit. In addition to CaC2 being a known carcinogen, it also contains traces of arsenic and phosphorus that can result in serious ramifications for human health. While detection may be possible by conventional methods such as chemical analysis or visual inspection, they may not be quick enough or convenient and thus not feasible. In this paper, we detect an artificially ripened banana non-invasively using multi-spectral imaging in eight narrow bands across the Visible (VIS) and Near-Infra-Red (NIR) spectrum, we construct multi-spectral images collected from artificially and naturally ripened samples of bananas. On a large scale data set consisting of 5760 samples, an experimental evaluation was conducted by performing 10 fold cross-validation. The average classification accuracy of 94.41 ± 4.70% based on spectral signature shows the significance of using multi-spectral images for detecting artificially ripened banana. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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43. Automated Cyberbullying Detection in Social Media Using an SVM Activated Stacked Convolution LSTM Network.
- Author
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Buan, Thor Aleksander and Ramachandra, Raghavendra
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- 2020
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44. Improved ear verification after surgery - An approach based on collaborative representation of locally competitive features
- Author
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Ramachandra, Raghavendra, Raja, Kiran, Venkatesh, Sushma Krupa, and Busch, Christoph
- Subjects
ComputingMethodologies_PATTERNRECOGNITION ,InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,otorhinolaryngologic diseases ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,sense organs ,GeneralLiterature_MISCELLANEOUS - Abstract
Ear characteristic is a promising biometric modality that has demonstrated good biometric performance. In this paper, we investigate a novel and challenging problem to verify a subject (or user) based on the ear characteristics after undergoing ear surgery. Ear surgery is performed to reconstruct the abnormal ear structures both locally and globally to beautify the overall appearance of the ear. Ear surgery performed for both for beautification and corrections alters the original ear characteristics to the greater extent that will challenge the comparison and subsequently verification performance of the ear recognition systems. This work presents a new database of images from 211 subjects with surgically altered ear along with corresponding pre and post-surgery samples. We then propose a novel scheme for ear verification based on the features extracted using a bank of filters learnt using Topographic Locally Competitive Algorithm (T-LCA) and comparison is carried out using Robust Probabilistic Collaborative Representation Classifier (R-ProCRC). Extensive experiments are carried out on both clean (normal) and surgically altered ear database to evaluate the performance of the proposed ear verification scheme. We also present a comprehensive performance analysis by comparing the performance of the proposed ear recognition scheme with eight different state-of-the-art ear verification system. Furthermore, we also present a new scheme to detect both deformed and surgically altered ear using one-class classification. Experimental results indicate the magnitude of problem in verifying the surgically altered ears and the signifies the need for considerable research in this direction.
- Published
- 2018
45. Cross-Eyed : Cross-Spectral Iris/Periocular Recognition Database and Competition
- Author
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Peter Wild, Fernando Alonso-Fernandez, James Ferryman, Christoph Busch, Lulu Chen, Ana F. Sequeira, Ramachandra Raghavendra, Kiran B. Raja, and Joseph Bigun
- Subjects
Computer science ,Iris recognition ,0211 other engineering and technologies ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Iris ,Signalbehandling ,02 engineering and technology ,computer.software_genre ,Databases ,Mediateknik ,Media Engineering ,Datorseende och robotik (autonoma system) ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Training ,Computer vision ,Iris (anatomy) ,Computer Vision and Robotics (Autonomous Systems) ,021110 strategic, defence & security studies ,Benchmark testing ,Database ,business.industry ,Medicinsk bildbehandling ,Medical Image Processing ,medicine.anatomical_structure ,ComputingMethodologies_PATTERNRECOGNITION ,Signal Processing ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
This work presents a novel dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. This database was used in the 1st Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed 2016). This competition aimed at recording recent advances in cross- spectrum iris and periocular recognition. Six submissions were evaluated for cross-spectrum periocular recognition, and three for iris recognition. The submitted algorithms are briefly introduced. Detailed results are reported in this paper, and comparison of the results is discussed.
- Published
- 2016
46. The 1st Competition on Counter Measures to Finger Vein Spoofing Attacks
- Author
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Norman Poh, Sébastien Marcel, Christoph Busch, Santosh Tirunagari, B. H. Shekar, Pedro Tome, Carlo Sansone, Diego Gragnaniello, Ramachandra Raghavendra, Luisa Verdoliva, Tome, P., Raghavendra, R., Busch, C., Tirunagari, S., Poh, N., Shekar, B. H., Gragnaniello, Diego, Sansone, Carlo, Verdoliva, Luisa, and Marcel, S.
- Subjects
Spoofing attack ,High security ,Biometrics ,business.industry ,Computer science ,Internet privacy ,Computer security ,computer.software_genre ,Finger vein recognition ,Counter measures ,Competition (economics) ,ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS ,business ,computer ,Vulnerability (computing) - Abstract
The vulnerability of finger vein recognition to spoofing attacks has emerged as a crucial security problem in the recent years mainly due to the high security applications where biometric technology is used. Recent works shown that finger vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate counter-measures against this type of fraudulent actions. The goal of the 1st Competition on Counter Measures to Finger Vein Spoofing Attacks is to challenge researchers to create counter-measures that can detect printed attacks effectively. The submitted approaches are evaluated on the Spoofing-Attack Finger Vein Database and the achieved results are presented in this paper.
- Published
- 2015
47. Baseline Evaluation of Smartphone based Finger-photo Verification System: A Preliminary Study of Technology Readiness.
- Author
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Wasnik, Pankaj, Stokkenes, Martin, Dunfjeldy, Mihkal, Ramachandra, Raghavendra, Raja, Kiran, and Busch, Christoph
- Subjects
SMARTPHONES ,BIOMETRIC identification ,DETECTORS ,HUMAN fingerprints ,BATTERY industry - Abstract
Increasingly, smartphones come embedded with more and more advanced biometric recognition systems. The high quality embedded sensors capture the biometric data which helps to get higher biometric recognition accuracy and security. On the other hand, these sensors increase the cost in terms of battery usage, take up extra physical space also some hardware cost. This paper investigates the reliability and effectiveness of fingerphoto ver-ification system developed using a smartphone. Consequently, biometric samples can be collected without the need for dedicated hardware, thus providing a cost-effective and re-liable way to do biometric authentication. The focus of this paper is to study various baselines to evaluate the readiness of the technology. To this extent, we present a com-parison between three non-commercial baseline systems with one commercial system. The commercial off-the-shelf (COTS) system shows the best performance of an equal error rate of 6.08%. [ABSTRACT FROM AUTHOR]
- Published
- 2018
48. Design and Development of Low-Cost Sensor to Capture Ventral and Dorsal Finger Vein for Biometric Authentication.
- Author
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Ramachandra, Raghavendra, Raja, Kiran Bylappa, Venkatesh, Sushma Krupa, and Busch, Christoph
- Abstract
Biometrics-based authentication of subjects is widely deployed in several real-life applications. Among various biometric characteristics, the finger-vein characteristic has demonstrated both reliable and highly accurate authentications for access control in secured applications. However, most of these systems are based on commercial sensors, where the image-level data are not available for academic research. In this paper, we present the design and development of a low-cost finger-vein sensor based on a single camera that can capture finger-vein images from dorsal and ventral parts of the finger with high quality. The system consists of multiple near-infrared (NIR) light sources to illuminate the finger from both sides (left and right) and top. The camera in the sensor is also coupled with the custom designed physical structure to facilitate high reflectance of the emitted light and distribute the light uniformly on the finger to capture good-quality dorsal and ventral finger-vein patterns. Extensive experiments are carried out on the data captured using the developed sensor and benchmarked the performance with eight different state-of-the-art (SOTA) algorithms. The results on a large-scale finger-vein dataset demonstrate the need for illumination from both sides (left and right) and from the top of the finger, to capture finger-vein images with high quality that improves the verification performance. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
49. Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey.
- Author
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RAMACHANDRA, RAGHAVENDRA and BUSCH, CHRISTOPH
- Subjects
- *
HUMAN facial recognition software , *BIOMETRIC identification , *HUMAN-computer interaction , *DATA security , *ELECTRONIC countermeasures - Abstract
The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community. The rapid evolution of face recognition systems into real-time applications has raised new concerns about their ability to resist presentation attacks, particularly in unattended application scenarios such as automated border control. The goal of a presentation attack is to subvert the face recognition system by presenting a facial biometric artifact. Popular face biometric artifacts include a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks. These have demonstrated a high security risk for state-of-the-art face recognition systems. However, several presentation attack detection (PAD) algorithms (also known as countermeasures or antispoofing methods) have been proposed that can automatically detect and mitigate such targeted attacks. The goal of this survey is to present a systematic overview of the existing work on face presentation attack detection that has been carried out. This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new PAD algorithms in a reproducible manner, and finally a summary of the relevant international standardization in this field. Furthermore, we discuss the open challenges and future work that need to be addressed in this evolving field of biometrics. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
50. Biometric Authentication Protocols on Smartphones.
- Author
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Stokkenes, Martin, Ramachandra, Raghavendra, and Busch, Christoph
- Published
- 2016
- Full Text
- View/download PDF
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