Fusion of face and iris features extraction based on steerable pyramid representation for multimodal biometrics

In this paper, we make a first attempt to combine face and iris biometrics using an efficient local appearance feature extraction method based on steerable pyramid (S-P), to captures the intrinsic geometrical structures of face and iris image, it decomposes the face and iris image into a set of directional sub-bands with texture details captured in different orientations at various scales. Local information is extracted from S-P sub-bands using block-based statistics to reduce the required amount of data to be stored. The obtained local features are combined at the score level for developing a multimode biometric approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system. We combine a face database FERET and iris database CASIA (version 1) to construct a multimodal biometric experimental database with which we validate the proposed approach and evaluate the multimodal biometrics performance. The experimental results reveal the multimodal biometric authentication is much more reliable and precise than single biometric approach.