An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Biometrical fingerprint recognition: don't get your fingers burned
Proceedings of the fourth working conference on smart card research and advanced applications on Smart card research and advanced applications
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Fingerprint Classification with Combinations of Support Vector Machines
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
A new approach to fake finger detection based on skin distortion
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Fake fingerprint detection by odor analysis
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Time-series detection of perspiration as a liveness test in fingerprint devices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Novel Region Based Liveness Detection Approach for Fingerprint Scanners
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Fingerprint liveness detection based on multiple image quality features
WISA'10 Proceedings of the 11th international conference on Information security applications
A high performance fingerprint liveness detection method based on quality related features
Future Generation Computer Systems
Journal of Intelligent Manufacturing
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This work introduces a new approach to detect fake fingers, based on the analysis of time-series fingerprint images. When a user puts a finger on the scanner surface, a time-series sequence of fingerprint images is captured. Five features are extracted from the image sequence. Two features represent the skin elasticity, and three features represent the physiological process of perspiration. Finally the Support Vector Matching (SVM) is used to discriminate the finger skin from other materials such as gelatin. The experiments carried out on a dataset of real and fake fingers show that the proposed approach and features are effective in fake finger detection.