Robust palmprint verification using sparse representation of binarized statistical features: a comprehensive study

Abstract

This paper proposes a new scheme for robust palmprint verification using sparse representation of Binarized Statistical Image Features (BSIF). Since palmprint comprises of rich set of features including principal lines, ridges and wrinkles, the use of appropriate texture descriptor is expected to accurately capture these information. To this extent, we explore the BSIF texture descriptor which codes each pixel of the given palmprint image in terms of binary strings based on the filter response. The BSIF learns the filter basis from the natural images by exploring statistical independence. We then use the Sparse Representation Classifier (SRC) on these BSIF features to perform the subject verification. Extensive experiments are carried out on three different large scale publically available palmprint databases. We then present an extensive analysis by comparing the proposed scheme with five different contemporary state-of-the-art schemes that reveals the outstanding performance.