Efficient Feature Points Detector for Full and Partial Palmprint Recognition

Over the last decade, biometrics has witnessed significant advancements in various forensic and security applications for human identification and authentication, with growing interest in effective and discriminative traits such as palmprints. However, practical applications still face challenges, especially when palmprints are collected in portions, such as at crime scenes, or partially acquired for authentication in uncontrolled environments. This paper presents a novel method that incorporates the local binary patterns (LBP) operator into the conventional scale-invariant feature transform (SIFT) algorithm to detect and extract robust keypoint features. While SIFT employs a Gaussian filter to detect keypoints on the palmprint, the proposed method leverages the multi-scale LBP operator to detect stable points prior to computing the corresponding descriptors. Furthermore, an efficient method for filtering keypoints, namely the Self-Geometric Relationship (SGR) filter, is introduced to eliminate potential false matches. The proposed palmprint recognition system, LBPSIFT-SGR, demonstrates competitive performance on full palmprints compared to state-of-the-art techniques and exhibits clear superiority on partial palmprint images, where competing systems fail, across different datasets.