Self-adaptive feature fusion method for improving LBP for face identification

Abstract

In a recent paper, a multi-scale information fusion method was presented to improve LBP for face identification. However, the additional parameters employed in that method cannot be automatically optimised. In this paper, a novel self-adaptive feature fusion method is proposed which extends the mLBP method by removing the need to optimise these parameters. Our method involves four steps. Firstly, a large number of initial features are generated. Then, we proposed a Fisher criteria-based method for evaluating the discriminative capabilities of different feature groups. After that, we proposed a model based on prism volume for selecting the optimal parameter combination. Finally, the resulting multi-scale feature are fused by a extended Euclidean distance fusion. Extensive experiments on two face databases have shown the proposed self-adaptive feature fusion method can find parameters that are optimal to the data in question, and can produce excellent classification performance.