Face recognition: A multivariate mutual information based approach

Abstract

A method based on multivariate mutual information (MMI) is proposed for face recognition. Unlike the existing frameworks, the proposed method is not hindered by rigorous computation for feature extraction and learning spaces. The proposed method uses information-theoretic framework for face recognition. The training set is used to estimate the underlying joint and marginal densities, which are utilized to calculate the mutual information. The mutual information for each pixel value is used to highlight the regions, that correspond to maximum information that are used for face recognition process. Performance of the proposed method is evaluated on two image datasets. The recognition performance of the proposed method is also compared with existing principal component analysis (PCA) based face recognition algorithms.