Hand-based single sample biometrics recognition

Currently, single sample biometrics recognition (SSBR) has emerged as one of the major research contents. It may lead to bad recognition result. To solve this problem, we present a novel approach by fusing two kinds of hand-based biometrics, i.e., palmprint and middle finger. We obtain their discriminant features by combining statistical information and structural information of each modal which are extracted using locality preserving projection (LPP) based on wavelet transform (WT). In order to reduce the influence of affine transform, we utilize mean filtering to enhance the robustness of structural information to improve the discriminant ability of palmprint high-frequency sub-bands. The two types of features are then fused at score level for the final hand-based SSBR. The experiments on the hand image database that contains 1,000 samples from 100 individuals show that the proposed feature extraction and fusion methods lead to promising performance.