Double Quantification of Template and Network for Palmprint Recognition

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 parameters both quantized fast light-weight recognition. binarized to compress weight accelerate speed. To avoid accuracy degradation caused by quantization, mutual information leveraged optimize ambiguity Hamming space obtain tri-valued code as template. Kleene Logic’s distance measures dissimilarity between templates. ablation experiments tested on binarization parameter, normalization trivialization output value. sufficient conducted several contact contactless datasets confirm multiple advantages our method.