RSNet: Region-Specific Network for Contactless Palm Vein Authentication

Abstract

More palm features, such as veins and shapes obtained from an enlarged contactless palm vein region of interest (ROI), have been shown to improve recognition performance. However, a few efforts have been made to adequately utilize these features for mining identity information. To address this issue, we propose a Region-Specific Network (RSNet) for contactless palm vein authentication. Our RSNet is a dual-branch structure for global and local feature extraction. Firstly, a Region-based Local feature Enhancement Block (RLEB) is proposed at the local branch to extract region-specific features. In the RLEB, the intermediate feature maps are divided into three asymmetrical patches based on the physiological characteristics of palm vein and palm shape for extracting diversified features, enhancing the local feature representation. Then, a Multi-scale Aggregation Block (MAB) is proposed that efficiently aggregates multi-scale features at a more granular level. Furthermore, to guide the global and local branches in learning complementary feature aspects, a difference loss is introduced to apply a soft subspace orthogonality constraint between the global and local vectors during training. The global branch is designed to assist the learning process of local features, without being adopted for inference. Extensive experiments have demonstrated the effectiveness and superiority of our method, and the RSNet achieves new State-Of-The-Art (SOTA) authentication performance on seven public contactless palm vein databases in the open-set scenario.