Deep Ternary Hashing Code for Palmprint Retrieval and Recognition

Abstract

Hashing has received more and more attention due to the characteristics of small storage and fast retrieval, especially in the field of biometric computing. However, there are only two types of binary relations, logical 'true' and logical 'false', which can't be distinguished well at the demarcation of these two logical relations in the Hamming space, resulting in ambiguity in the Hamming space neighborhood. Therefore, deep hash network is used to extract palmprint feature and optimize the ambiguity in Hamming space by using mutual information (MI) to obtain a trivialized palmprint hash code. The tri-valued Hamming distance using Kleene logic for matching reduces storage and improves matching speed compared to traditional local feature-based coding methods, and outperforms the binary palmprint deep hash coding. All the experiments are conducted on several contact/contactless palmprint and palm vein libraries, and extensive comparisons are made with several state-of-the-art methods, and the results demonstrate the effectiveness of the proposed scheme.