Symmetric Eigenfaces
نویسنده
چکیده
Abstract. Over the years, mathematicians and computer scientists have produced an extensive body of work in the area of facial analysis. Several facial analysis algorithms have been based on mathematical concepts such as the singular value decomposition (SVD). The SVD is generalized in this paper to take advantage of the mirror symmetry that is inherent in faces, thereby developing a new facial recognition algorithm: the symmetry preserving singular value decomposition (SPSVD). The SPSVD recognizes faces using half the total computations employed by the conventional SVD. Moreover, the SPSVD provides more accurate recognition, even in the presence of noise in the form of light variation and/or facial occlusion.
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