CR Loss: Improving Biometric Using ClassRoom Learning Approach

Abstract

Abstract One of the important factors in deep feature learning is their loss function design which highly influences the network performance. In this paper, we proposed a classroom (CR) learning approach along with arcface loss for contactless palmprint recognition to obtain high-level discriminative features without any extra load made to the network architecture. CR loss allows the network to learn the best possible feature representations for palmprint images. To validate our concept, we performed extensive experimental evaluations on various popular benchmark palmprint databases where our methods outperform the state-of-the-art methods. We also introduced a challenging contactless palmprint database called Harbin Institute of Technology-Network & Information Security Research Center contactless palmprint database version 1.0 (HIT-NIST-V1), as a new contribution to this domain. The result proves that the proposed CR loss consistently outperforms the SOTA methods for all the considered databases and especially for HIT-NIST-V1.