Near Infrared Face and Iris Fusion Algorithm Based on Support Vector Machine

Based on the near-infrared human face and iris. a fusion algorithm in the matching level was proposed. In the proposed algorithm, face was processed using two-dimensional principal component analysis (2DPCA) method based on wavelet transform for feature extraction and using Euclidean distance matching method for comparison. Iris was processed using the block-encoding method based on statistic of local information for feature extraction and using hamming distance matching method for comparison, was fused the match score using support vector machine (SVM) strategy in the matching level, and the fused matching score was used to make decision. The fusion algorithm was applied in a multi-model database, and, the experimental results show that the SVM fusion algorithm in matching level combines the advantages of the original biometric and even expresses a higher strength of the total recognition rate, which enhances the robustness of the multi-biometrics recognition system.