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
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Liveness Detection for Fingerprint Scanners Based on the Statistics of Wavelet Signal Processing
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Empirical Mode Decomposition Liveness Check in Fingerprint Time Series Captures
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Fake fingerprint detection by odor analysis
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Fake finger detection by skin distortion analysis
IEEE Transactions on Information Forensics and Security
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
Fake finger detection using the fractional Fourier transform
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Vitality detection in fingerprint identification
WSEAS Transactions on Information Science and Applications
Combining perspiration- and morphology-based static features for fingerprint liveness detection
Pattern Recognition Letters
A high performance fingerprint liveness detection method based on quality related features
Future Generation Computer Systems
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This paper proposes a novel method for fingerprint liveness detection based on band-selective Fourier spectrum. The 2D spectrum of a fingerprint image reflects the distribution and strength in spatial frequencies of ridge lines. The ridge-valley texture of the fingerprint produces a ring pattern around the center in the Fourier spectral image and a harmonic ring pattern in the subsequent ring. Both live and fake fingerprints produce these rings, but with different amplitudes in different spatial frequency bands. Typically, live fingerprints show stronger Fourier spectrum in the ring patterns than the fake. The proposed method classifies the live and the fake fingerprints by analyzing the band-selective Fourier spectral energies in the two ring patterns. The experimental results demonstrate this approach to be a promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing vulnerabilities.