The Experts below are selected from a list of 915 Experts worldwide ranked by ideXlab platform
Stephanie Caswell Schuckers - One of the best experts on this subject based on the ideXlab platform.
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new approach for liveness detection in Fingerprint Scanners based on valley noise analysis
Journal of Electronic Imaging, 2008Co-Authors: Bozhao Tan, Stephanie Caswell SchuckersAbstract:Recent research has shown that it is possible to spoof a variety of Fingerprint Scanners using some simple techniques with molds made from plastic, clay, Play-Doh, silicon, or gelatin materials. To protect against spoofing, methods of liveness detection measure physiological signs of life from Fingerprints, ensuring that only live fingers are captured for enrollment or authentication. We propose a new liveness detection method based on noise analysis along the valleys in the ridge-valley structure of Fingerprint images. Unlike live fingers, which have a clear ridge-valley structure, artificial fingers have a distinct noise distribution due to the material’s properties when placed on a Fingerprint Scanner. Statistical features are extracted in multiresolution scales using the wavelet decomposition technique. Based on these features, liveness separation (live/nonlive) is performed using classification trees and neural networks. We test this method on the data set, that contains about 58 live, 80 spoof (50 made from Play-Doh and 30 made from gelatin), and 25 cadaver subjects for 3 different Scanners. We also test this method on a second data set that contains 28 live and 28 spoof (made from silicon) subjects. Results show that we can get approximately 90.9–100% classification of spoof and live Fingerprints. The proposed liveness detection method is purely software-based, and application of this method can provide antispoofing protection for Fingerprint Scanners.
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time series detection of perspiration as a liveness test in Fingerprint devices
Systems Man and Cybernetics, 2005Co-Authors: Sujan T V Parthasaradhi, Reza Derakhshani, L A Hornak, Stephanie Caswell SchuckersAbstract:Fingerprint Scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in Fingerprint Scanners. This method quantifies a specific temporal perspiration pattern present in Fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other Fingerprint Scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof Fingerprint images. The dataset included Fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each Scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all Scanners, using the reduced time window and the more comprehensive training and test sets.
Schauff Axel - One of the best experts on this subject based on the ideXlab platform.
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steering handlebar has integrated electronic component s in form of pressure sensitive microswitch heat sensitive microswitch pressure sensitive foil switch or heat sensitive switch
2003Co-Authors: Schauff AxelAbstract:The steering handlebar has at least one integrated electronic component in the form of a pressure-sensitive microswitch, heat-sensitive microswitch, a pressure-sensitive foil switch or a heat sensitive switch. The electronic component can be a Fingerprint Scanner
Sujan T V Parthasaradhi - One of the best experts on this subject based on the ideXlab platform.
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time series detection of perspiration as a liveness test in Fingerprint devices
Systems Man and Cybernetics, 2005Co-Authors: Sujan T V Parthasaradhi, Reza Derakhshani, L A Hornak, Stephanie Caswell SchuckersAbstract:Fingerprint Scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in Fingerprint Scanners. This method quantifies a specific temporal perspiration pattern present in Fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other Fingerprint Scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof Fingerprint images. The dataset included Fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each Scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all Scanners, using the reduced time window and the more comprehensive training and test sets.
L A Hornak - One of the best experts on this subject based on the ideXlab platform.
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time series detection of perspiration as a liveness test in Fingerprint devices
Systems Man and Cybernetics, 2005Co-Authors: Sujan T V Parthasaradhi, Reza Derakhshani, L A Hornak, Stephanie Caswell SchuckersAbstract:Fingerprint Scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in Fingerprint Scanners. This method quantifies a specific temporal perspiration pattern present in Fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other Fingerprint Scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof Fingerprint images. The dataset included Fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each Scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all Scanners, using the reduced time window and the more comprehensive training and test sets.
Reza Derakhshani - One of the best experts on this subject based on the ideXlab platform.
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time series detection of perspiration as a liveness test in Fingerprint devices
Systems Man and Cybernetics, 2005Co-Authors: Sujan T V Parthasaradhi, Reza Derakhshani, L A Hornak, Stephanie Caswell SchuckersAbstract:Fingerprint Scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in Fingerprint Scanners. This method quantifies a specific temporal perspiration pattern present in Fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other Fingerprint Scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof Fingerprint images. The dataset included Fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each Scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all Scanners, using the reduced time window and the more comprehensive training and test sets.