Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
A deformable model for fingerprint matching
Pattern Recognition
Fingerprint matching based on global alignment of multiple reference minutiae
Pattern Recognition
A new approach to fake finger detection based on skin distortion
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
An algorithm for distorted fingerprint matching based on local triangle feature set
IEEE Transactions on Information Forensics and Security
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
Multi-scale local binary pattern with filters for spoof fingerprint detection
Information Sciences: an International Journal
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This paper introduces a novel method based on the elasticity analysis of the finger skin to discriminate fake fingers from real ones. We match the fingerprints before and after special distortion and gained their corresponding minutiae pairs as landmarks. The thin-plate spline (TPS) model is used to globally describe the finger distortion. For an input finger, we compute the bending energy vector by the TPS model and calculate the similarity of the bending energy vector to the bending energy fuzzy feature set. The similarity score is in the range [0, 1], indicating how much the current finger is similar to the real finger. The method realizes fake finger detection based on the normal steps of fingerprint processing without special hardware, so it is easily implemented and efficient. The experimental results on a database of real and fake fingers show that the performance of the method is available.