Robust Intra-Class Distance-Based Approach for Multimodal Biometric Game Theory-Based Rank-Level Fusion of Ear, Palmprint and Signature

Abstract

Biometrics is a branch of science which recognizes the person by using its behavioral and physiological characteristics. Limitations of unimodal biometric systems lead the researchers to investigate a system based on fusion of some unimodal biometric systems. In this paper, an innovative verification framework based on game theory-based rank-level fusion of the high frequency information of ear images and combination of dynamic information of online signatures with the texture information of palmprint image is proposed. In this framework, Gabor filter is used to extract locally the texture features of the ear, palmprint, and signature images. Moreover, intra-class distances are used to distinguish the genuine samples from the imposters, and also investigate the proper threshold to separate these two classes from each other. To fuse the unimodal biometrics, we use the game theory to obtain optimal logistic regression weights of rank-level fusion. For this purpose, using a three-player game and a novel multi-biometric model, the rank-level fusion of the ear, palmprint, and signature with optimal weights is carried out. The proposed verification framework is evaluated on SVC2004, PolyU and UND databases. The obtained results show the effectiveness and efficiency of our framework in comparison with other presented biometric fusion algorithms in the literature.