Pose-Robust Finger Identification Based on Mutual Region Alignment and Minimum Convolution Point Feature

<title>Abstract</title> <p>To address the performance degradation of finger identification systems caused by longitudinal rotation during contactless finger image acquisition, this paper proposes a novel pose-robust finger identification framework integrating mutual region extraction, minimum convolution point feature learning, and weighted score fusion. Specifically, longitudinal rotation of fingers easily leads to inconsistent imaging regions and non-rigid deformations, which severely undermine the stability of feature matching. To tackle these challenges, we propose a three-stage framework: First, a mutual region extraction strategy is proposed to adaptively align inconsistent imaging areas via vertical gradient calculation and minimum distance metric, effectively eliminating content discrepancies caused by longitudinal rotation. Second, a minimum convolution point feature learning module is designed to model non-linear correlations between cross-regional features, coupled with a deformation-aware matching algorithm to enhance robustness against finger deformation, mitigating the adverse impact of pose variations. Finally, a weighted score fusion method is introduced to integrate matching results from minimum convolution point features and Uniform Local Binary Pattern(LBP) features, fully exploiting the complementary information between structural and texture features. Extensive experiments are conducted on four public datasets, SDUMLA\_HMT, MMCBNU\_6000, FV-USM, and LFMB-3DFB, and the results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance in terms of recognition accuracy and robustness against longitudinal rotation and finger deformation.</p>