Information fusion of face and palmprint multimodal biometrics

The information fusion of face and palmprint biometrics using local features is investigated at feature level. The proposed method uses local information extracted from local region of biometric image which has rich statistical information. The texture of each region is processed using multiresolution analysis with different orientations and scales. The feature dimensionality of each region is reduced to produce a compact and high discriminative feature vector used for concatenation process. Feature fusion of the extracted features is able to increase the discrimination power in the feature space. The use of principle component analysis (PCA) and linear discriminant analysis (LDA) methods significantly reduce dimension of the feature vector by removing redundant and noise data while increasing the discriminant power in the fused feature space. Results of both identification and verification rates show significant improvement compared to that achieved by single modal biometrics and several existing multimodal methods.