A Hand-Based Multi-Biometrics via Deep Hashing Network and Biometric Graph Matching

Abstract

At present, the fusion of different unimodal biometrics has attracted increasing attention from researchers, who are dedicated to the practical application of biometrics. In this paper, we explored a multi-biometric algorithm that integrates palmprints and dorsal hand veins (DHV). Palmprint recognition has a rather high accuracy and reliability, and the most significant advantage of DHV recognition is the biopsy (Liveness detection). In order to combine the advantages of both and implement the fusion method, deep learning and graph matching were, respectively, introduced to identify palmprint and DHV. Upon using the deep hashing network (DHN), biometric images can be encoded as 128-bit codes. Then, the Hamming distances were used to represent the similarity of two codes. Biometric graph matching (BGM) can obtain three discriminative features for classification. In order to improve the accuracy of open-set recognition, in multi-modal fusion, the score-level fusion of DHN and BGM was performed and authentication was provided by support vector machine (SVM). Furthermore, based on DHN, all four levels of fusion strategies were used for multi-modal recognition of palmprint and DHV. Evaluation experiments and comprehensive comparisons were conducted on various commonly used datasets, and the promising results were obtained in this case where the equal error rates (EERs) of both palmprint recognition and multi-biometrics equal 0, demonstrating the great superiority of DHN in biometric verification.