Modular Image Principal Component Analysis for face recognition

One of the most successful process to accomplish human face recognition are the methods based on the principal component analysis (PCA), also known as eigenfaces. Recently, novel PCA approaches have been proposed: modular (MPCA) and two-dimensional (IMPCA). These approaches have achieved outstanding result in feature extraction and recognition. IMPCA is used for feature extraction based on 2D matrix representation and MPCA is based on image division to improve face recognition with variations like facial expressions, light and head pose. In this work we use some aspects of these methods to build a new technique called modular Image PCA (MIMPCA). The results achieved with the proposed method are superior in all experiments compared with the original techniques under different conditions of head pose angle, illumination and facial expression.