  In this paper, we propose an effective feature extraction algorithm, called
Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face
recognition. MS-CFB combines the benefits of global-based and local-based
feature extraction algorithms, where multiple correlation filters correspond-
ing to different face subregions are jointly designed to optimize the overall
correlation outputs. Furthermore, we reduce the computational complexi- ty of
MS-CFB by designing the correlation filter bank in the spatial domain and
improve its generalization capability by capitalizing on the unconstrained form
during the filter bank design process. MS-CFB not only takes the d- ifferences
among face subregions into account, but also effectively exploits the
discriminative information in face subregions. Experimental results on various
public face databases demonstrate that the proposed algorithm pro- vides a
better feature representation for classification and achieves higher
recognition rates compared with several state-of-the-art algorithms.
