1 June 2007 Maximum variance projections for face recognition
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Abstract
Maximum variance projection (MVP), as a novel subspace learning algorithm, is proposed. It is a linear discriminant algorithm that preserves local information by capturing the local geometry of the manifold. Two abilities of manifold learning and classification are combined into the properties of our algorithm. Since face images often belong to a submanifold of intrinsically low dimension, we carry out the MVP algorithm for face manifold learning and classification. Several experiments show the effectiveness of our developed algorithm.
© (2007) Society of Photo-Optical Instrumentation Engineers (SPIE)
Tianhao Zhang, Tianhao Zhang, Jie Yang, Jie Yang, Huahua Wang, Huahua Wang, Chunhua Du, Chunhua Du, } "Maximum variance projections for face recognition," Optical Engineering 46(6), 067206 (1 June 2007). https://doi.org/10.1117/1.2746880 . Submission:
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