Search

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)

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

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.

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

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

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.

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

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)

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.

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.

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.

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.

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.