The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
A Combination Fingerprint Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
An Analysis of Minutiae Matching Strength
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Enhancing security and privacy in biometrics-based authentication systems
IBM Systems Journal - End-to-end security
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
Texture classification using Curvelet Statistical and Co-occurrence Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Image denoising with complex ridgelets
Pattern Recognition
Computers in Biology and Medicine
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
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
The finite ridgelet transform for image representation
IEEE Transactions on Image Processing
Fingerprint liveness detection based on multiple image quality features
WISA'10 Proceedings of the 11th international conference on Information security applications
Expert Systems with Applications: An International Journal
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Perspiration phenomenon is very significant to detect liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore it may not be suitable for real-time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, we propose a new ridgelet transform-based method which needs only one fingerprint to detect liveness. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Ridgelet transform allows representing singularities along lines in a more efficient way than the wavelets. Fingerprint is an oriented texture pattern of ridge lines; hence naturally ridgelets are more suitable for fingerprint processing than the wavelets. We use ridgelet energy and co-occurrence signatures to characterize fingerprint texture using our databases consisting of real and spoof fingerprints. Dimensionalities of feature sets are reduced by running principal component analysis (PCA) algorithm. Ridgelet energy and co-occurrence signatures are independently tested on various classifiers such as: neural network, support vector machine and K-nearest neighbor. Finally, we fuse all the classifiers using the ''mean rule'' to build an ensemble classifier. Fingerprint databases consisting of 185 real, 90 fun-doh and 150 gummy fingerprints are created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the performance of a new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.