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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.

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.

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.

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.

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.

Contact lens detection in the eye is a significant task to improve the reliability of iris recognition systems. A contact lens overlays the iris region and prevents the iris sensor from capturing the normal iris region. In this paper, we present a novel scheme for detection to detecting a contact lens using Deep Convolutional Neural Network (CNN). The proposed CNN architecture ContlensNet is structured to have fifteen layers and configured for the three-class detection problem with the following classes: images with textured (or colored) contact lens, soft (or transparent) contact lens, and no contact lens. The proposed ContlensNet is trained using numerous iris image patches and the problem of overfitting the network is addressed by using the dropout regularization method. 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 2013 (ND) that are comprised of contact lens iris samples captured using four different sensors. The obtained results have demonstrated the improved performance of the proposed scheme with an average performance improvement of more than 10% in Correct Classification Rate (CCR%) when compared with eight different state-of-the-art contact lens detection systems.

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.

Biometric authentication based on face recognition acquired enormous attention due to its non-intrusive nature of image capture. Recently, with the advancement in sensor technology, face recognition based on Multi-spectral imaging has gained lot of popularity due to its potential of capturing discrete spatio-spectral images across the electromagnetic spectrum. Our contribution here is to study empirically, the extensive comparative performance analysis of 22 photometric illumination normalization techniques for robust Multi-spectral face recognition. To evaluate this study, we developed a Multi-spectral imaging sensor that can capture Multi-spectral facial images across nine different spectral band in the wavelength range from 530 nm to 1000 nm. With the developed sensor we captured Multi-spectral facial database for 231 individuals, which will be made available in the public domain for the researcher community. Further, quantitative experimental performance analysis in the form of identification rate at rank 1, was conducted on 22 photometric normalization techniques using four state-of-the-art face recognition algorithms. The performance analysis indicates outstanding results with utmost all of the photometric normalization techniques for six spectral bands such as 650 nm, 710 nm, 770 nm, 830 nm, 890 nm, 950 nm.

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.

Multi-spectral face recognition has been an active area of research over the past few decades. However, the vulnerability of multi-spectral face recognition systems is a growing concern that argues the need for Presentation Attack Detection (PAD) (or countermeasure or anti-spoofing) schemes to successfully detect targeted attacks. In this work, we present a novel feature descriptor L α MT i F that can effectively capture time-frequency features from the maximum response obtained on the high pass band image, which is obtained from the scale-space decomposition of the presented image. The proposed feature descriptor can effectively capture the micro-texture patterns that can be effectively used describe the variation from the presented image. We then propose a new framework using the proposed L α MT i F features that process the input multi-spectral face image independently. These extracted features are then classified using a linear Support Vector Machine (SVM) to obtain the binary decision. Finally, we carry out a decision fusion using the And rule to obtain the final decision. Extensive experiments are carried out on publicly available multi-spectral face datasets that have indicated the efficacy of the proposed scheme.

Biometric recognition of an individual can be done based on physiological characteristics such as a face, palmprint, vein, etc. and behavioural characteristics which include gait, keystroke, mouse dynamics, etc. Among the various biometric modalities, the wrist vein recognition has been known for high accuracy, stability and resistance to spoofing. The success of wrist vein biometrics strongly correlates with the quality of the wrist vein image captured using the sensor. In this paper, we present a new wrist vein sensor that can capture a high-quality wrist vein image using Near infrared (NIR) lighting. The NIR lighting employed in this work can emit light in a spectrum of 940nm that in turn is used to illuminate the wrist area of the hand. The presented sensor employs a single camera with a simple physical structure that will further improve the quality of the light to properly illuminate the wrist hand region to capture good quality wrist vein images. Extensive experiments are carried out on our newly collected database comprised of 50 subjects resulting in 100 unique wrist veins. We also present a comprehensive evaluation of nine different state-of-the-art techniques that demonstrated the outstanding performance of the Log-Gabor and Sparse Representation Classifier (SRC) with an EER of 1.63% and the GMR of 94.01 at FMR = 10−1%.

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.

Smartphone based periocular recognition has received substantial attention from the biometric research community. In this work, we propose a new scheme for the smartphone based periocular recognition. The proposed scheme is based on the texture features extracted from the periocular images using Maximum Response (MR) filters. These texture features are then classified using a deep neural network based on deeply coupled autoencoders. Extensive experiments are carried out on the large-scale VISOB database with 550 subjects captured using three different smartphones. The obtained results demonstrate the average performance of the proposed scheme with GMR of over 92% at FMR = 10−3.

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%.

Drug abuse leads to high degree of the change in the facial structure of a person due to multiple factors which include loss of fat in face, change of facial structure due to changes in facial muscles and change of skin texture due to appearance of acnes. The combination of such effects lead to completely deformed face which presents a complex challenge to face based authentication systems. To study the impact of the drug-abuse on face recognition systems, in this work, we create a new face database collected before and after the drug-abuse. The newly collected database which is referred as Drug Abuse Database (DAD) consists of images obtained from 101 subjects. Further, the collected database is analysed using various state-of-art face recognition algorithms along with a widely used commercial-off-the-shelf (COTS) system. The obtained performance of GMR = 12.90% at FMR = 0.01% indicates the challenge presented by such face images and underlines the importance of newer algorithms to handle such challenge.

Wide acceptance of biometrics as an authentication mode has led to investigation of multiple modalities such as face, periocular, iris for the long term robustness. Due to various deformities arising out of deteriorating health, need for enhancing the beauty by choice or to fix the injury as a result of trauma or aging, people tend to undergo surgery. However, such surgeries do not guarantee the restoration of physical biometric characteristics (face, periocular, iris etc.) to original appearance and thereby impacting the performance in biometric identifications. Among many physical biometric characteristics, periocular recognition is widely accepted for authentication purposes. This work studies the impact of periocular surgeries on biometric performance. To this extent, we introduce a new large scale periocular surgery database comprising of 402 unique periocular images acquired before and after the surgery. This is the first work that provides comprehensive study on evaluating the impact of surgeries in periocular region on periocular recognition. Extensive experiments are carried out on a newly created dataset using 11 different state-of-art periocular recognition schemes. Further, we also explore score level fusion of these algorithms. Results obtained on the newly created large scale database indicate the degraded identification performance of both the state-of-art and fusion algorithms.

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.

Applicability of the face recognition for smartphone-based authentication applications is increasing for different domains such as banking and e-commerce. The unsupervised data capture of face characteristics in biometric applications on smartphones presents the vulnerability to attack the systems using artefact samples. The threat of presentation attacks (a.k.a spoofing attacks) need to be handled to enhance the security of the biometric system. In this work, we present a new approach of using the raw sensor data. We first obtain the residual image corresponding to noise by subtracting the median filtered version of raw data and then computing simple energy value to detect the artefact based presentations. The presented approach uses simple threshold and thereby overcomes the need for learning complex classifiers which are challenging to work on unseen attacks. The proposed method is evaluated using a newly collected database of 390 live presentation attempts of face characteristics and 1530 attack presentations consisting of electronic screen attacks and printed attacks on the iPhone 6S smartphone. Significantly lower average classification error (

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.

Smartphones are gaining popularity as a biometric authentication device for many applications like banking and e-commerce. While multiple smartphone manufacturers providing phones with new features, consumers venture to try new phones or use multiple devices to sign into secure applications leading to a situation, in which they have to deal with data emerging from various smartphones along with manifold capture conditions. Recognition processes involving such cross-smartphone data leads to degraded biometric performance. In this work, we propose a novel technique to extract texture features from the periocular region by decomposing the images into Laplacian pyramids of various scales and obtain frequency responses in different orientations. Further, we propose to encode the features sparsely as a means to increase the cross-smartphone authentication using periocular region. From the extensive set of experiments conducted on a publicly available smartphone periocular database, we demonstrate the improvement using the proposed feature encoding and comparison for authentication scenarios. An average gain in Equal Error Rate of around 10 % is achieved while the best gain of 16% is obtained for various comparisons as compared to previously reported verification scores. The obtained gain in performance indicates the applicability of the proposed feature extraction technique for real-life authentication scenarios employing data from different smartphones in the visible spectrum.

Imaging dark iris in visible spectrum has had limited success due to low texture visibility owing to light-scattering and absorption properties of the cells in iris. Traditional iris imaging employ Near-infra-red (NIR) illumination to address such a problem, however, this limits the use of biometrics using regular cameras such as the ones present on smartphones. In this work, we propose a new iris imaging framework, to resolve the iris texture pattern without employing the NIR illumination. The proposed iris imaging setup employs a simple illumination source placed at an acute angle to axis of eye and imaging device to maximize the texture visibility. The proposed setup is used to obtain a new iris image database to evaluate the verification performance. The newly constructed iris image database of dark iris images comprises of 62 unique iris patterns with 10 samples each in different session. The database is acquired using iPhone 5S smartphone. Further a benchmark comparison is provided with respect to NIR images for the subset of the database to measure the robustness of proposed method. Detailed experiments are carried out using five well established state-of-art iris recognition algorithms and have indicated the superior performance of the proposed imaging setup with Genuine Match Rate (GMR) of 85.98% at False Match Rate (FMR) = 0.01% with an Equal Error Rate (EER) of 3.54%.

Biometric recognition involves in identifying the individual based on behavioural and biological characteristics. Among the various biometric modalities, the dorsal hand vein recognition has been known for high accuracy, stability and resistance to spoofing. The crucial fact, in achieving an accurate biometric recognition strongly correlates with the quality of the dorsal hand vein image that can be captured in real-life scenarios. In this paper, we present a new dorsal hand vein sensor that can capture a good quality of dorsal hand vein images. The introduced sensor is based on a near infrared illumination that can emit light in a spectrum of 940nm that in turn is used to illuminate the dorsal hand region. The presented sensor employs a single camera with a simple structure that will further improve the quality of the light to properly illuminate the dorsal hand region. Extensive experiments are carried out on our newly collected database comprised of 50 subjects resulting in 100 unique dorsal hand veins. We also present an extensive evaluation of eight different state-of-the-art techniques that demonstrated the outstanding performance of the Log-Gabor and Sparse Representation Classifier (SRC) with an EER of 0.7%.

Iris recognition in the visible spectrum has many challenging aspects. Especially, for subjects with dark iris color, which is caused by higher melanin pigmentation and collagen fibrils, the pattern is not clearly observable under visible light. Thus, the verification performance is generally lowered due to limited texture visibility in the captured iris samples. In this work, we propose a novel method of employing a white light-emitting-diode (LED) to obtain high-quality iris images with detailed texture. To evaluate the proposed set-up with LED light, we have acquired a new database of dark iris images comprising of 62 unique iris instances with ten samples each that were captured in different sessions. The database is acquired using three different smartphones - iPhone 5S, Nokia Lumia 1020 and Samsung Active S4. We also provide a benchmark of the proposed method with conventional to Near-Infra-Red (NIR) images, which are available for a subset of the database. Extensive experiments were carried out using five different well-established iris recognition algorithms and one commercial-of-the-shelf algorithm. They demonstrate the reliable performance of the proposed image capturing setup with GMR of 91.01% at FMR = 0.01% indicating the applicability in real-life authentication scenarios.

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.

Finger vein indexing refers to the process of creating clusters of finger vein samples based on the extracted features from the image. In large scale finger vein identification, the input probe image needs to be compared with a large set of gallery images to match the identity. Clusters can be used to confine the process of identity match of the images present in the gallery to the probe sample of the same cluster. In this work, we explored a new finger vein indexing and retrieval scheme with K-means clustering and pre-selection of features & comparison(PSFC). For a input probe sample, the centroid with the smallest distance will be chosen and compare with them for identification with K-means and P% of features (First, Last and Random) are compared with gallery samples features to choose for identification with PSFC. Extensive set off experiments are carried on a large scale data set of 2850 unique instances created using publicly available finger vein databases. Single cluster search with K-means clustering demonstrated the performance with pre-selection error of 9.38% (hit rate of 90.62%), Multi-cluster search with K-means clustering has achieved best performance with pre-selection error of 2.53% (hit rate of 97.47%) and PSFC has demonstrated the efficiency with pre-selection error of 9.58/9.85/8.05% (hit rate of 90.42/90.15/91.95%)for first/last/random features.

Automatic Border Control (ABC) systems employ biometrics for the identity verification of the travelers. Among the various biometric modalities, the face biometric is predominately used by considering its many advantages including contactless capture, non-intrusive and user-friendly interaction. Since the inflow of passengers at the airport is growing with time, the ABC systems are expected to work with high throughput. One way of addressing this is by performing the on-the-fly face recognition with ABC systems. However, due to various factors, including the change in illumination conditions, it is often challenging to capture a good quality face sample to facilitate a speedy biometric recognition with the ABC systems. One possible way to address this is by employing multiple cameras to capture different views of the face and then combine these views in the face recognition system. In this paper, we propose a framework for combining the information from multiple cameras to improve the face recognition accuracy. To this extent, we investigate five different fusion schemes to present an empirical study on a prototype version of MorphoWay™ ABC system. Extensive experiments are carried out on a database of 61 subjects that are recorded using a prototype version of MorphoWay™ ABC system in three different lighting conditions. The experimental results indicate the improvement in face recognition performance by combing information from multiple cameras in the prototype version of MorphoWay™ ABC system.

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.

Finger vein based biometric recognition has increasingly generated interest amongst biometric researchers because of the accuracy, robustness and anti-spoofing propertie. Prior efforts the are documented in the finger vein biometrics literature have only investigated the ventral vein pattern that is formed on the lower part of the finger underneath the skin surface. This paper investigates a new finger vein biometric approach by exploring the vein pattern that is present in the dorsal finger region. Thus, the dorsal finger vein pattern can be used as an independent biometric characteristic useful for the recognition of the target subject. We presented a complete automated approach with the key steps of image capturing, Region of Interest (ROI) extraction, pre-processing to enhance the vein pattern, feature extraction and comparison. This paper also introduces a new database of dorsal finger vein patterns from 125 subjects that resulted in 500 unique fingers with 10 samples each that results in a total of 5000 dorsal finger vein samples. Extensive experiments carried out on our new dorsal finger vein database achieve promising accuracy and thereby provide new insights on this new biometric approach.

Finger vein recognition has emerged as the robust biometric modality because of their unique vein pattern that can be captured using near infrared spectrum. The large scale finger vein based biometric solutions demand the need of searching the probe finger vein sample against the large collection of gallery samples. In order to improve the reliability in searching for the suitable identity in the large-scale finger vein database, it is essential to introduce the finger vein indexing and retrieval scheme. In this work, we present a novel finger vein indexing and retrieval scheme based on unsupervised clustering. To this extent we investigated three different clustering schemes namely K-means, K-medoids and Self Organizing Maps (SOM) neural networks. In addition, we also present a new feature extraction scheme to extract both compact and discriminant features from the finger vein images that are more suitable to build the indexing space. Extensive experiments are carried out on a large-scale heterogeneous finger vein database comprised of 2850 unique identities constructed using seven different publicly available finger vein databases. The obtained results demonstrated the efficacy of the proposed scheme with a pre-selection rate of 7.58% (hit rate of 92.42%) with a penetration rate of 42.48%. Further, the multi-cluster search demonstrated the performance with pre-selection error rate of 0.98% (hit rate of 99.02%) with a penetration rate of 52.88%.

The vulnerability of face recognition systems is a growing concern that has drawn the interest from both academic and research communities. Despite the availability of a broad range of face presentation attack detection (PAD) (or countermeasure or antispoofing) schemes, there exists no superior PAD technique due to evolution of sophisticated presentation attacks (or spoof attacks). In this paper, we present a new perspective for face presentation attack detection by introducing light field camera (LFC). Since the use of a LFC can record the direction of each incoming ray in addition to the intensity, it exhibits an unique characteristic of rendering multiple depth (or focus) images in a single capture. Thus, we present a novel approach that involves exploring the variation of the focus between multiple depth (or focus) images rendered by the LFC that in turn can be used to reveal the presentation attacks. To this extent, we first collect a new face artefact database using LFC that comprises of 80 subjects. Face artefacts are generated by simulating two widely used attacks, such as photo print and electronic screen attack. Extensive experiments carried out on the light field face artefact database have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well established state-of-the-art schemes.

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.

Highly unique pattern of the vein within the finger makes it robust biometrie characteristic which is instrumental in achieving low error rates for verification. Vascular pattern is reliable in many terms as they are characterized by very low error rates, good spoofing resistance and user convenience when compared with other existing biometrie modalities. In this paper, we present an extensive study of the state-of-the-art algorithms for vein recognition on a newly developed vein sensor. To this extent we collected a database using our newly designed finger vein imaging sensor. The database comprises of 1780 finger vein samples corresponding to 89 unique fingers from 32 subjects and benchmark the verification results on the newly composed database.

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.