Wavelet energy signature and GLCM features-based fingerprint anti-spoofing

This paper proposes a texture-based method to spoof-proof a fingerprint biometric system. The fundamental basis of this anti-spoofing method is that, real fingerprint exhibits different textural characteristics from a spoof one. Textural measures based on wavelet energy signatures and gray level co-occurrence matrix (GLCM) features are used to characterize fingerprint texture. Dimensionalities of the feature sets are reduced by running Pudilpsilas sequential forward floating selection (SFFS) algorithm. We test two feature sets independently on various classifiers like: AdaBoost.M1, support vector machine and OneR. Then, we fuse all the mentioned classifiers using the ldquoproduct rulerdquo to form a hybrid classifier. Classification rates achieved for wavelet energy signatures range from ~94.35% to ~96.71%. Likewise, classification rates for GLCM features range from ~94.82% to ~97.65%. Thus, the performance of a proposed method is very promising and it can be efficiently used to spoof-proof a real-time fingerprint biometric system.