Feature Level Fusion of Face, Profile Face and Ear for Efficient Multimodal Biometrics

Abstract

In multimodal biometric systems, the fusion of information is an important step. Information can be fused at the different levels, i.e., at the feature, matching or decision level, of the recognition system. In this research feature fusion of three biometric features viz. face, profile face, and ear has fused using discriminant correlation analysis. Discriminant correlation analysis performs an effective functional fusion through maximizing the pair correlations of the two feature sets and simultaneously eliminating correlations between classes and limiting correlations to the classes. In pattern recognition applications, the proposed method can be used to fuse features extracted from multiple modalities or combine different vectors extracted from a single modality. Many sets of experiments carried out on different biometric databases and using various techniques for extraction of the features demonstrate the effectiveness of the proposed method, which results in more advanced approaches.