Double Quantification of Template and Network for Palmprint Recognition

Abstract

The outputs of deep hash network (DHN) are binary codes, so DHN has high retrieval efficiency in matching phase and can be used for high-speed palmprint recognition, which is a promising biometric modality. In this paper, the templates and network parameters are both quantized for fast and light-weight palmprint recognition. The parameters of DHN are binarized to compress the network weight and accelerate the speed. To avoid accuracy degradation caused by quantization, mutual information is leveraged to optimize the ambiguity in Hamming space to obtain a tri-valued hash code as a palmprint template. Kleene Logic’s tri-valued Hamming distance measures the dissimilarity between palmprint templates. The ablation experiments are tested on the binarization of the network parameter, and the normalization and trivialization of the deep hash output value. The sufficient experiments conducted on several contact and contactless palmprint datasets confirm the multiple advantages of our method.