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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)

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)

Due to the proliferation of sports-related multimedia content on the WWW, effective visual search and retrieval present interesting research challenges. These are caused by poor image quality, a wide range of possible camera points of view, pose variations on the part of athletes engaged in playing a sport, deformations of text appearing on sports person’s clothing and uniforms in motion, occlusions caused by other objects, etc. To address these challenges, this paper presents a new method for detecting text on human bodies in sports images. Unlike most existing methods, which attempt to exploit locations of a player’s torso, face, and skin, we propose an end-to-end episodic learning approach that employs inductive learning criteria for detecting clothing regions in an image, which are, in turn, then used for text detection. Our method integrates a Residual Network (ResNet) and Pyramidal Pooling Module (PPM) for generating a spatial attention map. The Progressive Scalable Expansion Algorithm (PSE) is adapted for text detection from these regions. Experimental results on our own dataset as well as several benchmarks (like RBNR and MMM which contain images of runners in marathons, and Re-ID which is a person re-identification dataset) demonstrate that the proposed method outperforms existing methods in terms of precision and F1-score. We also present results for sports images chosen from natural scene text detection datasets such as CTW1500 and MS-COCO to show the proposed method is effective and reliable across a range of inputs.

Face morphing attacks have emerged as a potential threat, particularly in automatic border control scenarios. Morphing attacks permit more than one individual to use travel documents that can be used to cross borders using automatic border control gates. The potential for morphing attacks depends on the selection of data subjects (accomplice and malicious actors). This work investigates lookalike and identical twins as the source of face morphing generation. We present a systematic study on benchmarking the vulnerability of Face Recognition Systems (FRS) to lookalike and identical twin morphing images. Therefore, we constructed new face morphing datasets using 16 pairs of identical twin and lookalike data subjects. Morphing images from lookalike and identical twins are generated using a landmark-based method. Extensive experiments are carried out to benchmark the attack potential of lookalike and identical twins. Furthermore, experiments are designed to provide insights into the impact of vulnerability with normal face morphing compared with lookalike and identical twin face morphing., Comment: Accepted in IWBF 2023

Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements reported in recent works, we note that significant open issues such as independent benchmarking and lack of large-scale evaluation databases for improving the algorithms are inadequately addressed. The available databases are mostly of semi-public nature, lack of acquisition in the wild environment, and post-processing pipelines. Moreover, they do not represent a realistic capture scenario similar to real crime scenes, to benchmark the robustness of the algorithms. Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent. In this paper, we introduce a new wild large-scale latent fingerprint database that includes five different acquisition scenarios: reference fingerprints from (1) optical and (2) capacitive sensors, (3) smartphone fingerprints, latent fingerprints captured from (4) wall surface, (5) Ipad surface, and (6) aluminium foil surface. The new database consists of 1,318 unique fingerprint instances captured in all above mentioned settings. A total of 2,636 reference fingerprints from optical and capacitive sensors, 1,318 fingerphotos from smartphones, and 9,224 latent fingerprints from each of the 132 subjects were provided in this work. The dataset is constructed considering various age groups, equal representations of genders and backgrounds. In addition, we provide an extensive set of analysis of various subset evaluations to highlight open challenges for future directions in latent fingerprint recognition research., Comment: Submitted to IEEE Transactions on Information Forensics and Security (under review)

The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective., Comment: 29 pages, submitted to ACM computing survey journal

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.

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

Face Recognition Systems (FRS) are vulnerable to various attacks performed directly and indirectly. Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario. This work presents a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD) algorithm based on the spherical interpolation and hierarchical fusion of deep features computed from six different pre-trained deep Convolutional Neural Networks (CNNs). Extensive experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset by considering the real-life scenario of Automatic Border Control (ABC) gates. Experimental protocols are designed to benchmark the proposed and state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and capture distances. Obtained results have indicated the superior performance of the proposed D-MAD method compared to the existing methods., Comment: The paper is accepted at the International Joint Conference on Biometrics (IJCB) 2022

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.

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.

The growth of biometrics‐based authentication in various services raises the need to protect biometric data at the storage level. Specifically, biometric templates need to be protected after features are extracted to avoid the leakage of biometric data and subsequent linkability issues. An approach based on discrete hashing is presented with the assistance of semantic labels to generate discriminative and privacy preserving protected templates in this work. The proposed approach can easily be adopted for a closed‐enrolment set in which enrolment images are known a priori whereas the challenge of learning templates for a single subject remains open. To extend this approach for individual subject, the concept of auxiliary pseudouser enrolment data is introduced, through which a protected template can be generated at the user level. Through the use of a moderately sized multimodal biometric database of 94 subjects, the effectiveness of the proposed approach is illustrated to achieve a robust and secure template protection with irreversibility, unlinkability and renewability. With the set of experiments, the performance of the template protection approach is established and benchmarked against the popular bloom‐filter technique. The proposed approach results in a high genuine match rate (≈100% at a false accept rate of 0.01%) and low equal error rate (EER ≈ 0%) and outperforms traditional approaches while satisfying other requirements of biometric template protection when the closed enrolment set is known. With auxiliary pseudousers, the performance of the proposed approach for user‐level protected template creation results in an EER of 2.5%, indicating very low performance degradation compared with the known enrolment dataset. Along with the set of experimental validation of the proposed approach, a security analysis of the proposed approach is presented to demonstrate the unlinkability of the biometric templates using a state‐of‐art unlinkability metric.

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

Detecting multiple license plate numbers in crowded street scenes is challenging and requires the attention of researchers. In contrast to existing methods that focus on images that are not crowded with vehicles, in this work we aim at situations that are common and complex, for example, in city environments where numerous vehicles of different types like cars, trucks, motorbike etc. may present in a single image. In such cases, one can expect large variations in license plates in terms of quality, backgrounds, and various forms of occlusion. To address these challenges, we explore Adaptive Progressive Scale Expansion based Graph Attention Network (APSEGAT). This approach extracts local information which represents the license plates irrespective of vehicle types and numbers because it works at the pixel level in a progressive way, and identifies the dominant information in the image. This may include other parts of vehicles, drivers and pedestrians, and various other background objects. To overcome this problem, we integrate concepts of graph attention networks with progressive scale expansion networks. For evaluating the proposed method, we use our own dataset, named as AMLPR, which contains images captured in different crowded street scenes in different time span, and the benchmark dataset namely, UFPR-ALPR, which provides images of a single vehicle, and another benchmark dataset called, UCSD, which contains images of cars with different orientations. Experimental results on these datasets show that the method outperforms existing methods and is effective in detecting license plate numbers in crowded street scenes.

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

The technology of optical coherence tomography (OCT) to fingerprint imaging opens up a new research potential for fingerprint recognition owing to its ability to capture depth information of the skin layers. Developing robust and high security Automated Fingerprint Recognition Systems (AFRSs) are possible if the depth information can be fully utilized. However, in existing studies, Presentation Attack Detection (PAD) and subsurface fingerprint reconstruction based on depth information are treated as two independent branches, resulting in high computation and complexity of AFRS building.Thus, this paper proposes a uniform representation model for OCT-based fingerprint PAD and subsurface fingerprint reconstruction. Firstly, we design a novel semantic segmentation network which only trained by real finger slices of OCT-based fingerprints to extract multiple subsurface structures from those slices (also known as B-scans). The latent codes derived from the network are directly used to effectively detect the PA since they contain abundant subsurface biological information, which is independent with PA materials and has strong robustness for unknown PAs. Meanwhile, the segmented subsurface structures are adopted to reconstruct multiple subsurface 2D fingerprints. Recognition can be easily achieved by using existing mature technologies based on traditional 2D fingerprints. Extensive experiments are carried on our own established database, which is the largest public OCT-based fingerprint database with 2449 volumes. In PAD task, our method can improve 0.33% Acc from the state-of-the-art method. For reconstruction performance, our method achieves the best performance with 0.834 mIOU and 0.937 PA. By comparing with the recognition performance on surface 2D fingerprints, the effectiveness of our proposed method on high quality subsurface fingerprint reconstruction is further proved., Comment: 13 pages, 8 figures

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.

Fingerphoto images captured using a smartphone are successfully used to verify the individuals that have enabled several applications. This work presents a novel algorithm for fingerphoto verification using a nested residual block: Finger-NestNet. The proposed Finger-NestNet architecture is designed with three consecutive convolution blocks followed by a series of nested residual blocks to achieve reliable fingerphoto verification. This paper also presents the interpretability of the proposed method using four different visualization techniques that can shed light on the critical regions in the fingerphoto biometrics that can contribute to the reliable verification performance of the proposed method. Extensive experiments are performed on the fingerphoto dataset comprised of 196 unique fingers collected from 52 unique data subjects using an iPhone6S. Experimental results indicate the improved verification of the proposed method compared to six different existing methods with EER = 1.15%., Comment: a preprint paper accepted in wacv2023 workshop

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.

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

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.

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)

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.

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.

Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images so that an appropriate recognition method can be chosen accordingly based on the classification results for better performance. The proposed method explores local gradient differences for obtaining candidate pixels, which represent a stroke. To study the spatial distribution of candidate pixels, we propose a measure, called COLD, which is denser for pixels toward the center of strokes and scattered for non-stroke pixels. This observation leads us to introduce mass features for extracting the regular spatial pattern of COLD, which indicates a 2D text image. The extracted features are fed into a Neural Network (NN) for classification. The proposed method is tested on (i) a new dataset introduced in this work (ii) a second dataset assembled from standard natural scene datasets (iii) Non-Text Image datasets which does not contain text, rather it contains objects. Experimental results of the proposed method on images with text and non-text show that the proposed method is independent of text. The proposed approach improves text detection and recognition performance significantly after classification.

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 differences 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.

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

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

Identifying Tattoo is an integral part of forensic investigation and crime identification. Tattoo text detection is challenging because of its freestyle handwriting over the skin region with a variety of decorations. This paper introduces Deformable Convolution and Inception based Neural Network (DCINN) for detecting tattoo text. Before tattoo text detection, the proposed approach detects skin regions in the tattoo images based on color models. This results in skin regions containing Tattoo text, which reduces the background complexity of the tattoo text detection problem. For detecting tattoo text in the skin regions, we explore a DCINN, which generates binary maps from the final feature maps using differential binarization technique. Finally, polygonal bounding boxes are generated from the binary map for any orientation of text. Experiments on our Tattoo-Text dataset and two standard datasets of natural scene text images, namely, Total-Text, CTW1500 show that the proposed method is effective in detecting Tattoo text as well as natural scene text in the images. Furthermore, the proposed method outperforms the existing text detection methods in several criteria.

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multi-session, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the top-performing academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy., Comment: Accepted at International Join Conference on Biometrics (IJCB 2021)

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)

Augmented and virtual reality deployment is finding increasing use in novel applications. Some of these emerging and foreseen applications allow the users to access sensitive information and functionalities. Head Mounted Displays (HMD) are used to enable such applications and they typically include eye facing cameras to facilitate advanced user interaction. Such integrated cameras capture iris and partial periocular region during the interaction. This work investigates the possibility of using the captured ocular images from integrated cameras from HMD devices for biometric verification, taking into account the expected limited computational power of such devices. Such an approach can allow user to be verified in a manner that does not require any special and explicit user action. In addition to our comprehensive analyses, we present a light weight, yet accurate, segmentation solution for the ocular region captured from HMD devices. Further, we benchmark a number of well-established iris and periocular verification methods along with an in-depth analysis on the impact of iris sample selection and its effect on iris recognition performance for HMD devices. To the end, we also propose and validate an identity-preserving synthetic ocular image generation mechanism that can be used for large scale data generation for training purposes or attack generation purposes. We establish the realistic image quality of generated images with high fidelity and identity preserving capabilities through benchmarking them for iris and periocular verification.

Fingerprint recognition systems are widely deployed in various real-life applications as they have achieved high accuracy. The widely used applications include border control, automated teller machine (ATM), and attendance monitoring systems. However, these critical systems are prone to spoofing attacks (a.k.a presentation attacks (PA)). PA for fingerprint can be performed by presenting gummy fingers made from different materials such as silicone, gelatine, play-doh, ecoflex, 2D printed paper, 3D printed material, or latex. Biometrics Researchers have developed Presentation Attack Detection (PAD) methods as a countermeasure to PA. PAD is usually done by training a machine learning classifier for known attacks for a given dataset, and they achieve high accuracy in this task. However, generalizing to unknown attacks is an essential problem from applicability to real-world systems, mainly because attacks cannot be exhaustively listed in advance. In this survey paper, we present a comprehensive survey on existing PAD algorithms for fingerprint recognition systems, specifically from the standpoint of detecting unknown PAD. We categorize PAD algorithms, point out their advantages/disadvantages, and future directions for this area., Comment: Submitted to 3rd International Conference on Intelligent Technologies and Applications INTAP 2020

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.

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

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.

Ocular images have been used to supplement and complement the face-based biometrics. Ocular images are further investigated for identifying the gender of a person such that the soft label can be used to boost biometric performance of the system. Although there are number of works in visible spectrum and Near-Infrared spectrum for gender classification using ocular images, there are limited works in spectral imaging which explore ocular images for gender classification. Considering the advantages of spectral imaging, we explore the problem of gender identification using ocular images obtained using spectral imaging. To this end, we have employed a recent database of 104 unique ocular instances across 2 different sessions and 5 different attempts in each session with a spectral imaging camera capable of capturing 8 different images corresponding to different bands. Further, we present a new framework of using fused feature descriptors in kernalized space to fully leverage the number of spectral images for robust gender classification. With the set of experiments, we obtain an average classification accuracy of \(81\%\) with the proposed approach of using fused GIST features along with the weighted kernel representation of features in collaborative space.

Multi-patch based and hoslistic image deep sparse features for improved iris recognition using collaborative subspace.Explores color channel along with patch based approach for better feature representation.Extensive analysis & results presented for both MICHE-I & MICHE-II databases.High verification accuracy (MICHE-I & II) with single sample iris enrolment. The challenge of recognizing iris in visible spectrum images captured using smartphone stems from heavily degraded data (due to reflection, partial closure of eyes, pupil dilation due to light) where the iris texture is either not visible or visible to very low extent. In order to perform reliable verification, the set of extracted features should be robust and unique to obtain high similarity scores between different samples of same subject while obtaining high dissimilarity score between samples of different subjects. In this work, we propose multi-patch deep features using deep sparse filters to obtain robust features for reliable iris recognition. Further, we also propose to represent them in a collaborative subspace to perform classification via maximized likelihood, even under single sample enrolment. Through the set of extensive experiments on MICHE-I iris dataset, we demonstrate the robustness of newly proposed scheme which achieves high verification rate (GMR > 95%) with low Equal Error Rate (EER < 2%). Further, the robustness of proposed feature representation is reiterated by employing simple distance measures which has outperformed the state-of-art techniques. Additionally, the scheme is tested on the MICHE-II challenge evaluation dataset where the results are promising with GMR=100% on limited sub-corpus of iPhone data.

Given the need to protect the biometric data increasing due to recent enforcement of GDPR, we address the problem of protected template creation for iris recognition in this work. To this end, we introduce a new Morton Filter Based Template Protection for iris codes. The novel approach relies on creating a multi-bucket based protected template derived from single iris codes. By exploiting the low-rank iris codes from state-of-art iris feature extraction schemes to derive non-noisy iris bits, we design the new framework for creating protected templates by employing Morton Filters. The proposed template protection scheme is further evaluated on constrained and unconstrained iris recognition systems by employing two publicly available databases (IITD Iris and CASIA v4 Distance Dataset). Unlike previous works, to the best of our knowledge, this is the first work to demonstrate the applicability of template protection scheme in an unconstrained setting. The proposed approach achieves a low Equal Error Rate (EER ≈ 0%) on both constrained and unconstrained databases, exemplifying the scalability and adaptability. The approach being antagonistic to various configurations (block size and bit lengths), achieves a high degree of unlinkability deeming it practical for iris recognition systems.

The emergence of Full Field-Optical Coherence Tomography (FF-OCT) for fingerprint imaging has shown it's ability in addressing and solving the drawbacks of traditional fingerprinting solutions such as spoofing attacks, low accuracy for abraded fingerprint. With the availability of multiple internal fingerprints (from subsurface captured at different depths), it is also essential to consider the aspects of ideal biometrics where the privacy of the fingerprint data is preserved. In this work, we propose a new framework for fingerprint template protection, highly customized to FF-OCT by exploring the interplay between subsurface. As a first of it's kind work attempting template protection for FF-OCT fingerprints, we explore deeply learnt features to derive first level of template for subsurface fingerprint image. We further propose to intertwine subsurface level templates to provide better and robust templates. With the set of extensive experiments on a FF-OCT fingerprint database of 200 unique fingerprints with a total of 2400 images, we demonstrate reliable biometric performance resulting in EER of 5.69% for unprotected template at first layer (subsurface) of fingerprint in FF-OCT, an EER of 5.86% for the protected templates at same layer and EER of 5.08% with the final protected templates with proposed intertwining of subsurface fingerprint. Further, through the security analysis, we also validate the strength of the proposed approach with near ideal unlinkability.

Fingerprint-based biometric systems have shown reliability in terms of accuracy in both biometric and forensic scenarios. Although fingerprint systems are easy to use, they are susceptible to presentation attacks that can be carried out by employing lifted or latent fingerprints. This work presents a systematic study of the fingerprint presentation attack detection (PAD aka., spoofing detection) using textural features. To this end, this chapter reports an evaluation of both hand-crafted features and naturally learned features via deep learning techniques for fingerprint presentation attack detection. The evaluation is presented on publicly available fake fingerprint database that consists of both bona fide (i.e., real) and presentation attack fingerprint samples captured by capacitive, optical and thermal sensors. The results indicate the need for further approaches that can detect attacks across data from different sensors.

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.

Face recognition is widely employed in Automated Border Control (ABC) gates, which verify the face image on passport or electronic Machine Readable Travel Document (eMTRD) against the captured image to confirm the identity of the passport holder. In this paper, we present a robust morph detection algorithm that is based on differential morph detection. The proposed method decomposes the bona fide image captured from the ABC gate and the digital face image extracted from the eMRTD into the diffuse reconstructed image and a quantized normal map. The extracted features are further used to learn a linear classifier (SVM) to detect a morphing attack based on the assessment of differences between the bona fide image from the ABC gate and the digital face image extracted from the passport. Owing to the availability of multiple cameras within an ABC gate, we extend the proposed method to fuse the classification scores to generate the final decision on morph-attack-detection. To validate our proposed algorithm, we create a morph attack database with overall 588 images, where bona fide are captured in an indoor lighting environment with a Canon DSLR Camera with one sample per subject and correspondingly images from ABC gates. We benchmark our proposed method with the existing state-of-the-art and can state that the new approach significantly outperforms previous approaches in the ABC gate scenario., Comment: This work was accepted in The 15th International Conference on SIGNAL IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS, 2019

Smartphone-based biometrics authentication has been increasingly used for many popular everyday applications such as e-banking and secure access control to personal services. The use of biometric data on smartphones introduces the need for capturing and storage of biometric data such as face images. Unlike the traditional passwords used for many services, biometric data once compromised cannot be replaced. Therefore, the biometric data not only should not be stored as a raw image but also needs to be protected such that the original image cannot be reconstructed even if the biometric data is available. The transforming of raw biometric data such as face image should not decrease the comparison performance limiting the use of biometric services. It can therefore be deduced that the feature representation and the template protection scheme should be robust to have reliable smartphone biometrics. This chapter presents two variants of a new approach of template protection by enforcing the structure preserving feature representation via manifolds, followed by the hashing on the manifold feature representation. The first variant is based on the Stochastic Neighbourhood Embedding and the second variant is based on the Laplacian Eigenmap. The cancelability feature for template protection using the proposed approach is induced through inherent hashing approach relying on manifold structure. We demonstrate the applicability of the proposed approach for smartphone biometrics using a moderately sized face biometric data set with 94 subjects captured in 15 different and independent sessions in a closed-set scenario. The presented approach indicates the applicability with a low Equal Error Rate, \(EER = 0.65\%\) and a Genuine Match Rate, \(GMR = 92.10\%\) at False Match Rate (FMR) of \(0.01\%\) for the first variant and the second variant provides \(EER = 0.82\%\) and \(GMR = 89.45\%\) at FMR of \(0.01\%\). We compare the presented approach against the unprotected template performance and the popularly used Bloom filter template.

The need to protect biometric data within iris systems has resulted in a number of template protection schemes. A primary issue with current template protection schemes for iris recognition is the unavoidable biometric error rates, i.e., for any given False Non-Match Rate (FNMR) there is a high False Match Rate (FMR), especially at lower values of FNMR. In this work, we primarily focus on addressing this problem using a new approach with Cuckoo Filtering simultaneously using both stable bits and discriminative bits to derive a stronger template protection scheme. The proposed template protection scheme performs in a robust manner for various configurations as compared to earlier template protection schemes that need empirical fine-tuning. With the set of experiments on a publicly available iris dataset, we benchmark our results against the state-of-art template protection scheme based on Bloom-Filters. Specifically, we demonstrate the gain in performance and robustness of proposed approach at lower FNMR and invariance of performance to configurations of template protection scheme. With a specific configuration of proposed approach, we achieve Genuine Match Rate (GMR) = 100% at FMR = 0:01% and EER = 0% in the best case and GMR = 98:44% at FMR = 0:01% and EER = 0:33% in the worst case on IITD Iris database.

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.

Despite the challenge in recognizing disguise images on Face Recognition Systems (FRS), it has not received significant attention. Considering the fact that most of the FRS operates in visible spectrum which is not optimal solution for disguise face recognition, we explore spectral imaging and thereby the intrinsic features of spectral imaging for disguise face recognition. The major contribution of this work lies in presenting new approach that extract the histogram features independently for the spectral band images using Histogram Oriented Gradient (HOG) method and learning the unique discriminative features in affine feature space for disguise face recognition problem in robust manner. We present an extensive experimental validation results in terms of verification rate to present the significance of our proposed method. Further, we employ state-of-the-art disguise face recognition methods on individual spectral bands and fused spectral bands. The Proposed method demonstrates best performance in recognizing disguise face images compared to the state-of-the-art methods.

A light field sensor can provide useful information in terms of multiple depth (or focus) images, holding additional information that is quite useful for biometric applications. In this paper, we examine the applicability of a light field camera for biometric applications by considering two prominently used biometric characteristics: 1) face and 2) iris. To this extent, we employed a Lytro light field camera to construct two new and relatively large scale databases, for both face and iris biometrics. We then explore the additional information available from different depth images, which are rendered by light field camera, in two different manners: 1) by selecting the best focus image from the set of depth images and 2) combining all the depth images using super-resolution schemes to exploit the supplementary information available within the set elements. Extensive evaluations are carried out on our newly constructed database, demonstrating the significance of using additional information rendered by a light field camera to improve the overall performance of the biometric system.