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• W2952643772 abstract "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." @default.
• W2952643772 created "2019-06-27" @default.
• W2952643772 creator A5031492664 @default.
• W2952643772 creator A5044594971 @default.
• W2952643772 creator A5048411184 @default.
• W2952643772 date "2016-03-24" @default.
• W2952643772 modified "2023-10-16" @default.
• W2952643772 title "Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition" @default.
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• W2952643772 doi "https://doi.org/10.48550/arxiv.1603.07604" @default.
• W2952643772 hasPublicationYear "2016" @default.
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