Abstract: The recognition accuracy of the traditional biometric secure system is often influenced by the environment and physiological, and it leads to the development of the multimodal biometric systems. This paper presents a multi-modal biometric recognition system based on face and iris feature level fusion. Face and iris texture feature extraction is adopted Center-Symmetric Local Binary Pattern(CS-LBP) operators, the feature vectors that are extracted from face and iris are integrated linearly to form a mixed feature vector, and then Adaboost algorithm is used to select and aggregate the effective features from the mixed feature vector to build the strong classifier. Experimental results show that the system has better robustness than the mono-modal system.
multi-modal biometric recognition,
face and iris feature fusion,
feature description operator