The Experts below are selected from a list of 76413 Experts worldwide ranked by ideXlab platform
Christoph Busch - One of the best experts on this subject based on the ideXlab platform.
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Video Presentation Attack Detection in Visible Spectrum Iris Recognition Using Magnified Phase Information
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Kiran B. Raja, R. Raghavendra, Christoph BuschAbstract: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.
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SIN - An Empirical Study of Smartphone Based Iris Recognition in Visible Spectrum
Proceedings of the 7th International Conference on Security of Information and Networks - SIN '14, 2014Co-Authors: Kiran B. Raja, Ramachandra Raghavendra, Christoph Busch, Soumik MondalAbstract: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%.
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IJCB - Presentation attack detection on Visible Spectrum iris recognition by exploring inherent characteristics of Light Field Camera
IEEE International Joint Conference on Biometrics, 2014Co-Authors: Ramachandra Raghavendra, Christoph BuschAbstract: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.
Kiran B. Raja - One of the best experts on this subject based on the ideXlab platform.
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Video Presentation Attack Detection in Visible Spectrum Iris Recognition Using Magnified Phase Information
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Kiran B. Raja, R. Raghavendra, Christoph BuschAbstract: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.
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SIN - An Empirical Study of Smartphone Based Iris Recognition in Visible Spectrum
Proceedings of the 7th International Conference on Security of Information and Networks - SIN '14, 2014Co-Authors: Kiran B. Raja, Ramachandra Raghavendra, Christoph Busch, Soumik MondalAbstract: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%.
Romeo Bernini - One of the best experts on this subject based on the ideXlab platform.
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Slot and Layer-Slot Waveguide in the Visible Spectrum
Journal of Lightwave Technology, 2011Co-Authors: Genni Testa, Romeo BerniniAbstract:Slot waveguides based on TiO2 nanowires are numerically analyzed in the Visible Spectrum. The modal properties and the sensing capabilities of a conventional slot waveguide are investigated at an operating wavelength of 635 nm. Furthermore, a novel slot waveguide configuration called Layer-slot (L-slot) is proposed and discussed. The numerical results show that L-slot waveguides permit to achieve field confinement and sensing properties with values comparable with conventional slot waveguide, but with a strongly simplified fabrication process.
Ramachandra Raghavendra - One of the best experts on this subject based on the ideXlab platform.
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SIN - An Empirical Study of Smartphone Based Iris Recognition in Visible Spectrum
Proceedings of the 7th International Conference on Security of Information and Networks - SIN '14, 2014Co-Authors: Kiran B. Raja, Ramachandra Raghavendra, Christoph Busch, Soumik MondalAbstract: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%.
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IJCB - Presentation attack detection on Visible Spectrum iris recognition by exploring inherent characteristics of Light Field Camera
IEEE International Joint Conference on Biometrics, 2014Co-Authors: Ramachandra Raghavendra, Christoph BuschAbstract: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.
R. Raghavendra - One of the best experts on this subject based on the ideXlab platform.
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Video Presentation Attack Detection in Visible Spectrum Iris Recognition Using Magnified Phase Information
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Kiran B. Raja, R. Raghavendra, Christoph BuschAbstract: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.
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Smartphone based robust iris recognition in Visible Spectrum using clustered K-means features
2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings, 2014Co-Authors: Kira . Raja, R. Raghavendra, Christoph UschAbstract: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.
