Abstract
This paper builds the concept of kernel cuboid, and proposes a new kernel-based image feature extraction method for face recognition. The proposed method deals with a face image in a block-wise manner, and independently performs kernel discriminant analysis in every block set, using kernel cuboid instead of kernel matrix. Experimental results on the ORL and UMIST face databases show the effectiveness and scalability of the proposed method.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China under grant No. 61562017, the Scientific Research Foundation of Hainan University (Project No.: kyqd1443), and the Guangzhou Zhujiang Science and Technology Future Fellow Fund (Grant No. 2012J2200094).
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Communicated by A. Di Nola.
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Liu, XZ., Zhang, CG. Fisher discriminant analysis based on kernel cuboid for face recognition. Soft Comput 20, 831–840 (2016). https://doi.org/10.1007/s00500-015-1794-2
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DOI: https://doi.org/10.1007/s00500-015-1794-2