A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors

Palmprint recognition is a relatively new and effective biometric technology. Most of the traditional palmprint recognition methods are based on contact acquisition devices, which affects their user-friendliness and limits their applications. To overcome these shortcomings, this work proposes a contactless palmprint verification approach based on SIFT, which is composed of three steps, namely, image preprocessing, SIFT feature extraction and matching, and matching refinement. Palmprint images are firstly preprocessed using an isotropic filter, and then the SIFT points are detected and matched. Finally, the matched points are refined by employing a two-stage strategy. In the first stage, an iterative RANSAC (I-RANSAC) algorithm is employed to remove the mis-matched points which fail to satisfy the topological relations. In the second stage, local palmprint descriptors (LPDs) are extracted for SIFT points to further remove the mis-matched points which cannot be distinguished by original SIFT descriptors. The number of final matched SIFT points is taken as the score for decision. Experimental results show that the proposed approach is effective in contactless palmprint recognition, especially when non-linear deformations exist in palmprint images.