Search

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.

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

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)

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.

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.

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.

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.

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.

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.

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

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.

The gaining popularity of the visible spectrum iris recognition has sparked the interest in adopting it for various access control applications. Along with the popularity of visible spectrum iris recognition comes the threat of identity spoofing, presentation, or direct attack. This paper presents a novel scheme for detecting video presentation attacks in visible spectrum iris recognition system by magnifying the phase information in the eye region of the subject. The proposed scheme employs modified Eulerian video magnification (EVM) to enhance the subtle phase information in eye region and novel decision module to classify it as artefact(spoof attack) or normal presentation. The proposed decision module is based on estimating the change of phase information obtained from EVM, specially tailored to detect presentation attacks on video-based iris recognition systems in visible spectrum. The proposed scheme is extensively evaluated on the newly constructed database consisting of 62 unique iris video acquired using two smartphones—iPhone 5S and Nokia Lumia 1020. We also construct the artefact database with 62 iris acquired by replaying normal presentation iris video on iPad with retina display. Extensive evaluation of proposed presentation attack detection (PAD) scheme on the newly constructed database has shown an outstanding performance of average classification error rate = 0% supporting the robustness of the proposed PAD scheme.

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 the 2nd Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward. © 2017 IEEE SIDUS-AIR FastPass PROTECT HFACE SWAN

Good biometric performance of iris recognition motivates it to be used for many large scale security and access control applications. Recent works have identified visible spectrum iris recognition as a viable option with considerable performance. Key advantages of visible spectrum iris recognition include the possibility of iris imaging in on-the-move and at-a-distance scenarios as compared to fixed range imaging in near-infra-red light. The unconstrained iris imaging captures the images with largely varying radius of iris and pupil. In this work, we propose a new segmentation scheme and adapt it to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation. The proposed technique has shown the improved segmentation accuracy up to 85% with standard OSIRIS v4.1. This work also proposes a new feature extraction method based on deepsparsefiltering to obtain robust features for unconstrained iris images. To evaluate the proposed segmentation scheme and feature extraction scheme, we employ a publicly available database and also compose a new iris image database. The newly composed iris image database (VSSIRIS) is acquired using two different smartphones - iPhone 5S and Nokia Lumia 1020 under mixed illumination with unconstrained conditions in visible spectrum. The biometric performance is benchmarked based on the equal error rate (EER) obtained from various state-of-art schemes and proposed feature extraction scheme. An impressive EER of 1.62% is obtained on our VSSIRIS database and an average gain of around 2% in EER is obtained on the public database as compared to the well-known state-of-art schemes.

Vulnerability of iris recognition systems remains a challenge due to diverse presentation attacks that fail to assure the reliability when adopting these systems in real-life scenarios. In this paper, we present an in-depth analysis of presentation attacks on iris recognition systems especially focusing on the photo print attacks and the electronic display (or screen) attack. To this extent, we introduce a new relatively large scale visible spectrum iris artefact database comprised of 3300 iris normal and artefact samples that are captured by simulating five different attacks on iris recognition system. We also propose a novel presentation attack detection (PAD) scheme based on multiscale binarized statistical image features and linear support vector machines. Extensive experiments are carried out on four different publicly available iris artefact databases that have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well-established state-of-the-art schemes.

Face biometrics is widely used in various applications including border control and facilitating the verification of travellers' identity claim with respect to his electronic passport (ePass). As in most countries, passports are issued to a citizen based on the submitted photo which allows the applicant to provide a morphed face photo to conceal his identity during the application process. In this work, we propose a novel approach leveraging the transferable features from a pre-trained Deep Convolutional Neural Networks (D-CNN) to detect both digital and print-scanned morphed face image. Thus, the proposed approach is based on the feature level fusion of the first fully connected layers of two D-CNN (VGG19 and AlexNet) that are specifically fine-tuned using the morphed face image database. The proposed method is extensively evaluated on the newly constructed database with both digital and print-scanned morphed face images corresponding to bona fide and morphed data reflecting a real-life scenario. The obtained results consistently demonstrate improved detection performance of the proposed scheme over previously proposed methods on both the digital and the print-scanned morphed face image database.

Fingerprint recognition has been extensively used in numerous civilian applications ranging from border control to everyday identity verification. The threats to current systems emerge from two facts that can be attributed to potential loss in accuracy due to damaged external fingerprints and attacks on the sensors by creation of an artefacts (e.g. silicone finger) simply by lifting the latent fingerprints. In the growing need for attack resistant biometric fingerprint recognition that can be operated without supervision, a new generation of sensors has been investigated, which can capture the subsurface fingerprint pattern. In this work, we explore a subsurface fingerprint imaging technique by employing a custom-built in-house Full-Field Optical Coherent Tomography (FF-OCT) sensor for capturing the subsurface fingerprint. Further, we evaluate a newly constructed database of 200 unique fingerprint samples collected in 2 different sessions with 6 layers of fingerprint images corresponding to 6 subsurface fingerprints. We also propose a framework based on quality metrics to fuse the subsurface fingerprint images to achieve a robust verification accuracy, which has resulted in Equal Error Rate (EER) of 0%. We also provide an extensive set of experiments to gauge the reliability of subsurface fingerprint recognition and deduce a set of important conclusions for the path forward in FFOCT subsurface fingerprint imaging.

Multispectral face recognition systems are widely used in various access control applications. The vulnerability of multispectral face recognition sensors towards low-cost Presentation Attack Instrument (PAI) such as printed photos used in attacks has emerged as a serious security threat. In this paper, we present a novel framework to detect presentation attacks against an extended multispectral face sensor. The proposed framework stems from the idea of exploring the complementary information available from different bands of an extended multispectral face sensor. To this extent, two different frameworks are proposed where the first framework is based on image fusion and the second builds on the Presentation Attack Detection (PAD) score level fusion. Extensive experiments are carried out on the extended multispectral face sensor database comprising of 50 subjects with two different presentation attacks generated using the printed photo artefacts. The obtained results indicate the superior performance of the PAD score level fusion on detecting both known and unknown attacks.

With the availability of sensor technology across the broad electromagnetic spectrum, multi-spectral imaging is increasingly used in biometric systems. Especially for face recognition, multi-spectral imaging has gained a lot of attention due to it's invariant property against variation caused by unknown illumination. However, obtaining best performance using multi-spectral imaging is still a challenge due to presence of a modality gap between the spectral imaging data and redundant band information. In this paper, we propose a fused band representation with a set of selected bands represented in Quaternion space for spectral band images to efficiently maintain the inter band relationship in spatial domain. The selection is based on measuring the information content in bands using entropy and fusion is carried out in Quaternion space for three best bands. The features from newly obtained image is collaboratively represented to achieve robust performance. The proposed approach is experimentally validated on the extended multi-spectral face database of 168 subjects, whose spectral band images are captured in 9 narrow spectral bands in visible and near infrared range (530nm to 1000nm). The quantitative performance analysis, obtained using the proposed method indicates 96.13% recognition rate at Rank-1, outperforming other state-of-the-art methods.

Presentation attack on the face recognition systems is well studied in the biometrics community resulting in various techniques for detecting the attacks. A low-cost presentation attack (e.g. print attacks) on face recognition systems has been demonstrated for systems operating in visible, multispectral (visible and near infrared spectrum) and extended multispectral (more than two spectral bands spanning from visible to near infrared space, commonly in 500nm-1000nm). In this paper, we propose a novel method to detect the presentation attacks on the extended multispectral face recognition systems. The proposed method is based on characterising the reflectance properties of the captured image through the spectral signature. The spectral signature is further classified using the linear Support Vector Machine (SVM) to obtain the decision on presented sample as an artefact or bona-fide. Since the reflectance property of the human skin and the artefact material differ, the proposed method can efficiently detect the presentation attacks on the extended multispectral system. Extensive experiments are carried out on a publicly available extended multispectral database (EMSPAD) comprised of 50 subjects with two different Presentation Attack Instrument (PAI) generated using two different printers. The comparison analysis is presented by comparing the performance of the proposed scheme with the contemporary schemes based on the image fusion and PAD score level fusion. Based on the obtained results, the proposed method has indicated the best performance in detecting both known and unknown attacks.

Multi-spectral imaging has recently acquired significant attention in biometrics based authentication due to it’s potential ability to capture spatio-spectral images across the electromagnetic spectrum. Especially, in the case of facial biometrics, multi-spectral imaging has shown significant promising results under unknown/varying illumination environment. However, the challenge arises when surveillance cameras provide the visible images while the enrollment are spectral band images. In order to address the backward/cross compatibility of probing visible images from regular surveillance cameras against the high quality spectral band images in enrollment, development of robust algorithms are required. In this paper, we present a new approach of selecting optimal band based on highest correlation coefficients of individual feature vectors from bands in comparison with feature vectors from visible images of respective individual classes for robust recognition performance. The proposed approach of band selection is validated on a newly collected face database of 168 subjects whose face images are collected in 9 different spectral bands and correspondingly their visible images from a regular camera operating in visible spectrum. The extensive set of experiments conducted on the new database with selected single band and multiple spectral bands in enrollment data versus the visible probe image has indicated the significance of the band selection. The new approach of spectral to visible matching with the proposed band selection method shows significant Rank- 1 recognition rate of 94.04% supporting the applicability of proposed method

Morphed face images are artificially generated images, which blend the facial images of two or more different data subjects into one. The resulting morphed image resembles the constituent faces, both in visual and feature representation. If a morphed image is enroled as a probe in a biometric system, the data subjects contributing to the morphed image will be verified against the enroled probe. As a result of this infiltration, which is referred to as morphed face attack, the unambiguous assignment of data subjects is not warranted, i.e. the unique link between subject and probe is annulled. In this work, we investigate the vulnerability of biometric systems to such morphed face attacks by evaluating the techniques proposed to detect morphed face images. We create two new databases by printing and scanning digitally morphed images using two different types of scanners, a flatbed scanner and a line scanner. Further, the newly created databases are employed to study the vulnerability of state-of-the-art face recognition systems with a comprehensive evaluation.

Presentation attacks (a.k.a, direct attacks or spoofing attacks) against face recognition systems have emerged as a serious security threat. To mitigate these attacks on conventional face recognition systems, several Presentation Attack Detection (PAD) algorithms have been developed, which address various Presentation Attack Instruments (PAI) including 3D face masks, 2D photo, wrap photo and electronic display, that can be used for the attack. In this paper, we demonstrate and evaluate the vulnerability of an extended Multispectral face recognition system. The extended Multispectral system captures the face image across various spectral bands, thus, we propose to study each of these spectral bands for the vulnerability towards presentation attacks. We have employed a commercial Multispectral camera - SpectraCam™ that can capture seven different spectral bands to collect both bona-fide (a.k.a, live or normal or real) samples as well as artefact (or spoof) face samples. Extensive experiments are carried out on the newly compiled database to provide insights on the vulnerability of the extended Multispectral face system towards PAI generated using a printer. We have created the face artefacts using two different printers, which include laser and inkjet printers. Further, we have also evaluated the state-of-the-art PAD algorithms that are widely employed in conventional face PAD systems. Our study reveals the vulnerability of extended Multispectral face recognition system with respect to the print attack. The results obtained using state-of-the-art PAD algorithms further indicate the challenge to detect the presentation attacks in extended Multispectral face recognition systems.

Multispectral palmprint is considered as an effective biometric modality to accurately recognize a subject with high confidence. This paper presents a novel multispectral palmprint recognition system consisting of three functional blocks namely: (1) novel technique to extract Region of Interest (ROI) from the hand images acquired using a contact less sensor (2) novel image fusion scheme based on dependency measure (3) new scheme for feature extraction and classification. The proposed ROI extraction scheme is based on locating the valley regions between fingers irrespective of the hand pose. We then propose a novel image fusion scheme that combines information from different spectral bands using a Wavelet transform from various sub-bands. We then perform the statistical dependency analysis between these sub-bands to perform fusion either by selection or by weighted fusion. To effectively process the information from the fused image, we perform feature extraction using Log-Gabor transform whose feature dimension is reduced using Kernel Discriminant Analysis (KDA) before performing the classification by employing a Sparse Representation Classifier (SRC). Extensive experiments are carried out on a CASIA multispectral palmprint database that shows the strong superiority of our proposed fusion scheme when benchmarked with contemporary state-of-the-art image fusion schemes. HighlightsNovel adaptive weighted image fusion scheme to combine different multi-spectral palmprints.Dynamic switching between selection and weighted fusion to achieve high accuracy.Novel Scheme for palmprint ROI extraction based on hand shape.Extensive qualitative and quantitative analysis of the proposed (ROI & fusion) scheme.Benchmarking with well known state-of-the-art schemes that include 5 different contemporary multi-spectral image fusion schemes and 2 well known state-of-the-art palmprint recognition schemes.

Periocular characteristics are being used as a supplementary feature for the biometric systems employing iris characteristics to mitigate effects of the noisy iris on authentication performance. In the same lines, ocular characteristics are also used to enhance the performance of face based systems under the impact of pose, expression and illumination. However, the iris and face systems are operated in Near-Infra-Red (NIR) and visible spectrum respectively. In order have both the systems work for ocular images and be compatible to each other, the biometric system needs to be robust enough to handle the biometric data emerging from different spectrum. In this work, we employ an ocular image database collected using the visible and NIR cameras. We propose a new framework employing a bank of Binarized Statistical Image filters along with χ2 distance metric along with simple fusion to handle the cross-spectrum data. Set of experiments conducted on the cross-spectrum periocular database indicate the robustness of the system with the achieved GMR of 96.04% at the FMR of 0.01%. The obtained performance indicates the applicability of proposed framework for realistic cross-spectrum biometric authentication scenario.

Multi-spectral face recognition has acquired significant attention over a last few decades due to its potential of capturing spatial and spectral information across the electromagnetic spectrum. In this paper, we present a new imaging scheme that can obtain the multi-spectral face image at nine different spectra covering 530nm–1000nm. We prepared a new database comprising of 230 subjects using our new low-cost multi-spectral face imaging device. Extensive experiments are presented for evaluating the performance of the four different state-of-the-art face recognition algorithms on both individual bands and the fused spectral face image. Obtained results show the improved face recognition performance of Log-Gabor features with Collaborative Representation (CRC) as the classifier.

Widespread deployment of Automatic Border Control (ABC) along with the electronic Machine Readable Travel Documents (eMRTD) for person verification has enabled a prominent use case of face biometrics in border control applications. Many countries issue eMRTD passports on the basis of a printed biometric face photo submitted by the applicant. Some countries offer web-portals for passport renewal, where citizens can upload their face photo. These applications allow the possibility of the photo being altered to beautify the appearance of the data subject or being morphed to conceal the applicant identity. Specifically, if an eMRTD passport is issued with a morphed facial image, two or more data subjects, likely the known applicant and one or more unknown companion(s), can use such passport to pass a border control. In this work we propose a novel scheme to detect morphed face images based on facial micro-textures extracted using statistically independent filters that are trained on natural images. Given a face image, the proposed method will obtain a micro-texture variation using Binarized Statistical Image Features (BSIF), and the decision is made using a linear Support Vector Machine (SVM). This is first work carried out towards detecting the morphed face images. Extensive experiments are carried out on a large-scale database of 450 morphed face images created using 110 unique subjects with different ethnicity, age, and gender that indicates the superior performance.

The challenges of presentation attacks (spoofing attacks) at sensor level is increasing for biometric systems due to the evolving method of artefact presentation. The sophisticated attacks now employ high quality printed artefacts and electronic screens to present the biometric samples which make it difficult to separate the real presentations and artefact presentations. In this work, we propose a new scheme to detect the artefacts in both NIR and visible spectrum biometric sensors for ocular biometric systems using a new set of feature descriptor. The scheme employs adaptive and quantized texture patters obtained from local microfeatures and global spatial features for different color channels in an image. Further, the texture descriptors are used to learn a spectrally regressed discriminant classifier to classify the normal ocular images against the artefact ocular images. The proposed scheme is used to perform extensive experiments on 5 publicly available ocular datasets including 2 datasets acquired in NIR domain and two datasets acquired using smartphones along with a dataset acquired using high quality camera. The experiments conducted on all the datasets have consistently indicated the robust performance against attacks by showing a classification error of 0%.

Multispectral face images captured in more than one spectra is known to provide reliable person verification, especially in varying illumination conditions. In this paper, we present an extended multi-spectral face recognition framework by combining the face images captured in six different spectra consisting of 425nm,475nm,525nm,570nm,625nm, and 680nm. We propose a novel image fusion scheme that combines the information from different spectrum of multispectral face images. The proposed image fusion scheme first selects two images from set of all spectral images based on the highest information quantified using entropy measure. Two selected images are combined by decomposing them using Discrete Wavelet Transform (DWT) to get the sub-bands that are fused using weighted sum rule. The weights are computed automatically on each of these sub-bands by measuring the dependency using correlation and wavelet energy. Extensive experiments are carried out on a newly constructed exclusive multispectral face database using a commercial multispectral sensor SpectraCamTM from Pixelteq company. Extensive experiments are carried out on our database to present both qualitative and quantitative results of the proposed image fusion scheme. The comprehensive comparative analysis is performed by comparing the performance of the proposed scheme with four different state-of-the-art schemes. The obtained results have justified the efficacy of the proposed system for robust multispectral face recognition.

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.

Finger vein biometric systems have shown a vulnerability to presentation attacks (or direct attack or spoof attack). The presentation attacks have raised crucial security issues for finger vein biometric systems mainly due to the widespread application of these biometric sensors in the financial sector. In this paper, we present a novel algorithm to detected the presentation attack on a finger vein recognition system. The proposed method relies on the texture analysis using steerable pyramids, and the final decision is carried out by classifying the textures features using Support Vector Machines (SVM). Furthermore, we have also introduced a new kind of attack by using a smartphone display for the first time. Extensive experiments are carried out on a large-scale database of 300 unique finger veins collected from 100 subjects. We introduce a new finger vein artefact database collected using three different kinds of artefacts namely: (1) ink jet print (2) laser jet print and (3) smartphone display. An extensive comparison of the proposed scheme with ten different Presentation Attack Detection (PAD) demonstrated the efficacy of the proposed scheme.

Palmprint is a widely used biometric trait deployed in various access-control applications due to its convenience in use, reliability, and low cost. In this paper, we propose a novel scheme for palmprint recognition using a sparse representation of features obtained from Bank of Binarized Statistical Image Features (B-BSIF). The palmprint image is characterized by a rich set of features including principal lines, ridges, and wrinkles. Thus, the use of an appropriate texture descriptor scheme is expected to capture this information accurately. To this extent, we explore the idea of B-BSIF that comprises of 56 different BSIF filters whose responses on the given palmprint image is processed independently and classified using sparse representation classifier (SRC). Extensive experiments are carried out on three different large-scale publicly available palmprint databases. We then present an extensive analysis by comparing the proposed scheme with seven different contemporary state-of-the-art schemes that reveals the efficacy of the proposed scheme for robust palmprint recognition.

Although face recognition is a widely accepted form of biometrics due to its ease of capture, it still suffers from out-of-focus imaging due to the limited depth-of-field found in conventional 2D imaging systems. In order to overcome such a challenge, light field cameras have been successfully employed in biometrics. The number of depth images obtained from a light field camera can be optimally used by fusing a number of these images or selecting the best focus image from the set of images. In this work, we employ a method to pre-select the images when more than two images corresponding to different focus are present. Further, we evaluate various fusion techniques against the all − in − focus image obtained from light field imaging for the verification performance in face recognition systems. The evaluation of various schemes is performed on one of the largest light field face databases consisting of 80 subjects. The best Equal Error Rate (EER) of 4.14% is observed with the combination of employed pre-selection method and Laplacian Pyramid based fusion approach using both sparse representation and multi-scale transform.

Contact lenses are known to degrade the performance of the iris recognition system. Thus, accurate detection of a contact lens is of paramount importance not only in improving the reliability but also the security of an iris recognition system. In this paper we present a novel scheme for detecting contact lenses in iris images. The proposed scheme is based on an ensemble of statistically independent filters whose response on the iris image is processed and further classified using a linear Support Vector Machine (SVM) to detect the contact lens. Extensive experiments are carried out on two publicly available large scale databases, namely: IIIT-Delhi Contact lens iris database (IIITD) and Notre Dame cosmetic contact lens database 2012 (ND) that are comprised of contact lens iris samples captured using four different sensors. The rigorous experiments conducted in this work show the outstanding performance of the proposed scheme, especially in detecting a textured contact lens with a Correct Classification Rate (CCR) of 100%. We also present a comprehensive benchmark of the proposed scheme with six different well established state-of-the-art schemes available for the contact lens detection.

Smartphones are increasingly used as biometrie sensor for many authentication applications due to the computational ability and high resolution cameras that can be used to capture biometrie information. The objective of this paper is to assess the performance of iris versus periocular recognition for smartphones in non ideal conditions (change of illumination, highly pigmented iris, shadows on iris pattern) in real-life for verification in visible spectrum. We introduce various protocols for real-life verification scenarios using smartphones for iris and periocular recognition. Further, we also study the verification performance where enrollment and probe data originate from different smartphones. From the extensive set of experiments conducted on a publicly available smartphone database, it can be observed that the information from periocular region provides substantially good performance in terms of recognition accuracy in cross sensor and varying illumination scenarios as compared to iris under same conditions.

Smartphones and tablet computers are being actively studied for the performance of biometric recognition in visible spectrum. Owing to robust performance of iris recognition, many works have investigated the performance in visible spectrum. Increasing popularity of iris recognition in the visible spectrum has further resulted in using smartphones for the same. The extraction of robust features for visible spectrum iris recognition is vital to meet the expected accuracy of recognition. In this work, we explore K - means clustering based feature extraction to obtain robust features. K-means clustering is a fast alternative training method that is not computationally expensive and can easily be extended to large scale systems. The robust features extracted serves best for the unconstrained iris recognition on smartphones in visible spectrum. The proposed feature extraction technique has been extensively evaluated on publicly available smartphone iris database from BIPLab. The best Equal Error Rate of 0.31% is achieved using the proposed technique on images captured using iPhone in indoor scenario.

The face biometric systems are highly vulnerable for the presentation attack that can be carried out by presenting a photo or video or even a 3D mask. In this paper, we present a novel Presentation Attack Detection (PAD) algorithm that can accurately detect and mitigate the 3D mask attacks on a face recognition system. The proposed scheme extracts both local and global features from the captured face image. The local features employed in this work corresponds to the eye (periocular) and nose region that are expected to provide clue on the presence of the mask. In addition, we also capture the micro-texture variation as a global feature using Binarized Statistical Image Features (BSIF). We then train a linear Support Vector Machine (SVM) independently on these two features whose scores are fused using the weighted sum rule before making the decision about a real face or an artefact. Extensive experiments are carried out on the public 3D mask database 3DMAD that shows the superiority of the proposed scheme with an outstanding performance of HTER = 0.03%.

The vascular pattern of the finger has emerged as a promising new biometric modality, characterized by very low error rates, good spoofing resistance and user convenience when compared with other existing biometric modalities. In this paper, we present a new sensor design that is not only cost effective but also robust enough to capture the finger vascular (or finger vein) pattern. We evaluate the previously proposed finger vein verification approaches and also propose a new scheme that illustrates its superiority over existing approaches. Extensive experiments are carried out on the newly developed database constructed using our newly designed finger vein imaging sensor that comprises of 1780 finger vein samples corresponding to 89 unique fingers from 32 subjects. The data is collected in two different scenarios shows the efficacy of the proposed sensor as well as the proposed scheme. I. INTRODUCTION Biometric system involves in establishing the human iden- tity based on the physical and behavioural characteristics. The adoptability of the biometric system in the large scale appli- cations like India's UIDAI (Aadhar) and UAE's port of entry shows the effectiveness of biometrics for real time identity solutions. Among the various biometric characteristics that are available, the finger vascular pattern (or finger vein) biometrics has received a substantial attention from both academia and industry. The finger vascular pattern system uses information about the blood vessel structures inside the finger. As a con- sequence, it offers a wide range of advantages including: (1) More convenient and ease to capture (2) Unique patterns and high verification rate (3) Difficult to spoof. However, capturing a high quality sample of finger vein is still a challenging task as it demands the adequate amount of light to penetrate or reflect from the finger. Further, brightness within the finger may also vary as finger structure is not equally thick. These factors motivate us to explore a new imaging device to reliably observe the prominent information from the fingervein that in turn can be used to build a biometric system using finger vein samples to achieve robust identity verification.

The advanced technologies and sensors in smartphones has led to showcase their potential as a biometric sensor. In this work, we present the feasibility study and challenges in the path forward for using smartphone as a biometric sensor for iris recognition in visible spectrum. Especially, with a limited shelf-life of smartphones, it is anticipated to have enrolment and verification using different camera. In this work, we propose an improvement to segmentation scheme for contactless iris acquisition by approximating the radius range. The proposed method has resulted in a segmentation accuracy of 81%. We also propose various protocols for real-life verification scenarios using smartphones for visible spectrum iris recognition. Finally, results from an extensive set of experiments are presented to validate the anticipated challenges in using smartphone based iris recognition. Being the first of its kind, this work provides the benchmarking results for the smartphone iris database. The best EER is obtained for iPhone in indoor scenario with an impressive EER of 0.48%.

Presentation (or spoof) attacks on biometric system is a growing concern that received substantial attention from both academics and industry. In this paper, we present a novel way of addressing a Presentation Attack Detection (PAD) (or spoof detection) by exploiting the inherent characteristics of the Light Field Camera (LFC) for visible spectrum iris biometric system. The proposed PAD algorithm will capture the variation in the depth (or focus) between multiple depth images rendered by the LFC that in turn can be used to reveal the presentation attacks. To this extent, we introduce a new presentation attack database comprised of 52 subjects with 104 unique eye samples. The database is collected using LFC by simulating the attacks through visible spectrum iris biometric artefacts like printed photo and electronic display (using both Apple iPad (4th generation) and Samsung Galaxy Note 10.1 tablet). Extensive experiments carried out on this database reveal the efficacy of the proposed PAD algorithm with a lowest Average Classification Error Rate = 0.5% when confronted with diverse set of attacks on visible spectrum iris biometric system.

Multimodal biometric systems based on fingerprint and finger vein modality provide promising features useful for robust and reliable identity verification. In this paper, we present a robust imaging device that can capture both fingerprint and finger vein simultaneously. The presented low-cost sensor employs a single camera followed by both near infrared and visible light sources organized along with the physical structures to capture good quality finger vein and fingerprint samples. We further present a novel finger vein recognition algorithm that explores both the maximum curvature method and Spectral Minutiae Representation (SMR). Extensive experiments are carried out on our newly collected database that comprises of 1500 samples of fingerprint and finger vein from 150 unique fingers corresponding to 41 subjects. Our results demonstrate the efficacy of the proposed sensor with a lowest Equal Error Rate of 0.78%.