Joint Finger Valley Points-Free ROI Detection and Recurrent Layer Aggregation for Palmprint Recognition in Open Environment

Cooperative palmprint recognition, pivotal for civilian and commercial uses, stands as the most essential and broadly demanded branch in biometrics. These applications, often tied to financial transactions, require high accuracy in recognition. Currently, research in palmprint recognition primarily aims to enhance accuracy, with relatively few studies addressing the automatic and flexible palm region of interest (ROI) extraction (PROIE) suitable for complex scenes. Particularly, the intricate conditions of open environment, alongside the constraint of human finger skeletal extension limiting the visibility of Finger Valley Points (FVPs), render conventional FVPs-based PROIE methods ineffective. In response to this challenge, we propose an FVPs-Free Adaptive ROI Detection (FFARD) approach, which utilizes cross-dataset hand shape semantic transfer (CHSST) combined with the constrained palm inscribed circle search, delivering exceptional hand segmentation and precise PROIE. Furthermore, a Recurrent Layer Aggregation-based Neural Network (RLANN) is proposed to learn discriminative feature representation for high recognition accuracy in both open-set and closed-set modes. The Angular Center Proximity Loss (ACPLoss) is designed to enhance intra-class compactness and inter-class discrepancy between learned palmprint features. Overall, the combined FFARD and RLANN methods are proposed to address the challenges of palmprint recognition in open environment, collectively referred to as RDRLA. Experimental results on four palmprint benchmarks HIT-NIST-V1, IITD, MPD and BJTU_PalmV2 show the superiority of the proposed method RDRLA over the state-of-the-art (SOTA) competitors. The code of the proposed method is available at https://github.com/godfatherwang2/ RDRLA.