Weber pattern and Binarized Statistical Image Features encoded CNN for fingerprint liveness detection

This paper presents a new fingerprint liveness detection system based on local features Simplified Weber Local Descriptor (SWLD) and Binarized Statistical Image Features (BSIF) as a local textural descriptor encoded CNN models. SWLD feature and BSIF feature vector are combined together to ensure the preservation of the local intensity information and the orientation gradient in WLD is substituted by a recent Binarized Statistical Image Features (BSIF). After an appropriate feature selection, a trained linear-kernel SVM classifier makes the final live/fake decision. Our experimental analysis on two publicly available databases LivDet2011 and LivDet2013, comprising datasets collected from various sensors, show that the classification performance of our proposed method is effective and meanwhile, provides a better detection accuracy compared with the majority of previously published results, which are or not based on the binary pattern descriptors.