The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform

Anil K. Jain - One of the best experts on this subject based on the ideXlab platform.

  • Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
    Co-Authors: Jie Li, Shiguang Shan, Anil K. Jain, Xilin Chen
    Abstract:

    The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary Biometric Traits (e.g., face and fingerprint) based person identification, a single Biometric Trait alone can not meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft Biometric Traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.

  • Biometric recognition sensor characteristics and image quality
    IEEE Instrumentation & Measurement Magazine, 2011
    Co-Authors: Salil Prabhakar, A Ivanisov, Anil K. Jain
    Abstract:

    Biometric recognition, or simply Biometrics, refers to recognizing a person based on one or more of his anatomical or behavioral characteristics. A good Biometric Trait should be measurable, distinctive (different for every person) and stable over time. The sensing method should not be intrusive or socially unacceptable, and the system should be easy to use. A Biometric system based on this Trait should be accurate, fast, robust and inexpensive. In this article, we discuss the signal acquisition aspects of fingerprint and iris Biometrics-two of the most widely used Biometric Traits.Personal recognition of people is necessary to conduct many social and economic activities. Besides visual recognition of acquaintances, checking a person's government issued photo ID is the most common procedure. In electronic access or transactions, passwords and security tokens are commonly used. These credentials are surrogates of a person's identity Their major shortcoming is that they can be easily compromised by being lost, stolen, or given to someone else. For better security, it is necessary to link the digital identity of a person to his body's characteristics.

  • periocular Biometrics in the visible spectrum
    IEEE Transactions on Information Forensics and Security, 2011
    Co-Authors: Unsang Park, Arun Ross, Raghavender Jillela, Anil K. Jain
    Abstract:

    The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular Biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular Biometric Traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a Biometric Trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular Biometrics, 6) use of the periocular Biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.

  • Continuous user authentication using temporal information
    Proceedings of SPIE, 2010
    Co-Authors: Koichiro Niinuma, Anil K. Jain
    Abstract:

    Conventional computer systems authenticate users only at the initial log-in session, which can be the cause of a critical security flaw. To resolve this problem, systems need continuous user authentication methods that continuously monitor and authenticate users based on some Biometric Trait(s). We propose a new method for continuous user authentication based on a Webcam that monitors a logged in user's face and color of clothing. Our method can authenticate users regardless of their posture in front of the workstation (laptop or PC). Previous methods for continuous user authentication cannot authenticate users without Biometric observation. To alleviate this requirement, our method uses color information of users' clothing as an enrollment template in addition to their face information. The system cannot pre-register the clothing color information because this information is not permanent. To deal with the problem, our system automatically registers this information every time the user logs in and then fuses it with the conventional (password) identification system. We report preliminary authentication results and future enhancements to the proposed system.

  • Biometric template transformation a security analysis
    Proceedings of SPIE, 2010
    Co-Authors: Abhishek Nagar, Anil K. Jain, Karthik Nandakumar
    Abstract:

    One of the critical steps in designing a secure Biometric system is protecting the templates of the users that are stored either in a central database or on smart cards. If a Biometric template is compromised, it leads to serious security and privacy threats because unlike passwords, it is not possible for a legitimate user to revoke his Biometric identifiers and switch to another set of uncompromised identifiers. One methodology for Biometric template protection is the template transformation approach, where the template, consisting of the features extracted from the Biometric Trait, is transformed using parameters derived from a user specific password or key. Only the transformed template is stored and matching is performed directly in the transformed domain. In this paper, we formally investigate the security strength of template transformation techniques and define six metrics that facilitate a holistic security evaluation. Furthermore, we analyze the security of two wellknown template transformation techniques, namely, Biohashing and cancelable fingerprint templates based on the proposed metrics. Our analysis indicates that both these schemes are vulnerable to intrusion and linkage attacks because it is relatively easy to obtain either a close approximation of the original template (Biohashing) or a pre-image of the transformed template (cancelable fingerprints). We argue that the security strength of template transformation techniques must consider also consider the computational complexity of obtaining a complete pre-image of the transformed template in addition to the complexity of recovering the original Biometric template.

Matjaž Branko Jurič - One of the best experts on this subject based on the ideXlab platform.

  • inertial sensor based gait recognition a review
    Sensors, 2015
    Co-Authors: Sebastijan Sprager, Matjaž Branko Jurič
    Abstract:

    With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a Biometric Trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.

Xilin Chen - One of the best experts on this subject based on the ideXlab platform.

  • Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
    Co-Authors: Jie Li, Shiguang Shan, Anil K. Jain, Xilin Chen
    Abstract:

    The explosive growth of digital images in video surveillance and social media has led to the significant need for efficient search of persons of interest in law enforcement and forensic applications. Despite tremendous progress in primary Biometric Traits (e.g., face and fingerprint) based person identification, a single Biometric Trait alone can not meet the desired recognition accuracy in forensic scenarios. Tattoos, as one of the important soft Biometric Traits, have been found to be valuable for assisting in person identification. However, tattoo search in a large collection of unconstrained images remains a difficult problem, and existing tattoo search methods mainly focus on matching cropped tattoos, which is different from real application scenarios. To close the gap, we propose an efficient tattoo search approach that is able to learn tattoo detection and compact representation jointly in a single convolutional neural network (CNN) via multi-task learning. While the features in the backbone network are shared by both tattoo detection and compact representation learning, individual latent layers of each sub-network optimize the shared features toward the detection and feature learning tasks, respectively. We resolve the small batch size issue inside the joint tattoo detection and compact representation learning network via random image stitch and preceding feature buffering. We evaluate the proposed tattoo search system using multiple public-domain tattoo benchmarks, and a gallery set with about 300K distracter tattoo images compiled from these datasets and images from the Internet. In addition, we also introduce a tattoo sketch dataset containing 300 tattoos for sketch-based tattoo search. Experimental results show that the proposed approach has superior performance in tattoo detection and tattoo search at scale compared to several state-of-the-art tattoo retrieval algorithms.

Sebastijan Sprager - One of the best experts on this subject based on the ideXlab platform.

  • inertial sensor based gait recognition a review
    Sensors, 2015
    Co-Authors: Sebastijan Sprager, Matjaž Branko Jurič
    Abstract:

    With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a Biometric Trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.

Arun Ross - One of the best experts on this subject based on the ideXlab platform.

  • periocular Biometrics in the visible spectrum
    IEEE Transactions on Information Forensics and Security, 2011
    Co-Authors: Unsang Park, Arun Ross, Raghavender Jillela, Anil K. Jain
    Abstract:

    The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular Biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular Biometric Traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a Biometric Trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular Biometrics, 6) use of the periocular Biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.

  • iris segmentation using geodesic active contours
    IEEE Transactions on Information Forensics and Security, 2009
    Co-Authors: Samir Shah, Arun Ross
    Abstract:

    The richness and apparent stability of the iris texture make it a robust Biometric Trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris structure. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. However, it is difficult to segment iris images acquired under nonideal conditions using such conic models. In this paper, we describe a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures. Since active contours can 1) assume any shape and 2) segment multiple objects simultaneously, they mitigate some of the concerns associated with traditional iris segmentation models. The proposed scheme elicits the iris texture in an iterative fashion and is guided by both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA v3.0 and WVU nonideal iris databases indicate the efficacy of the proposed technique.

  • periocular Biometrics in the visible spectrum a feasibility study
    International Conference on Biometrics: Theory Applications and Systems, 2009
    Co-Authors: Unsang Park, Arun Ross, Anil K. Jain
    Abstract:

    Periocular Biometric refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular Biometric does not require high user cooperation and close capture distance unlike other ocular Biometrics (e.g., iris, retina, and sclera). We study the feasibility of using periocular images of an individual as a Biometric Trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set that can be used for matching. The effect of fusing these feature sets is also studied. The experimental results show a 77% rank-1 recognition accuracy using 958 images captured from 30 different subjects.

  • information fusion in Biometrics
    Pattern Recognition Letters, 2003
    Co-Authors: Arun Ross, Anil K. Jain
    Abstract:

    User verification systems that use a single Biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the Biometric Trait and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. MultiBiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems help achieve an increase in performance that may not be possible using a single Biometric indicator. Further, multiBiometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple Biometric Traits simultaneously. However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts. This paper addresses the problem of information fusion in Biometric verification systems by combining information at the matching score level. Experimental results on combining three Biometric modalities (face, fingerprint and hand geometry) are presented.