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In Two-Dimensional Linear Discriminant Analysis (2DLDA), it is satisfied that within-class covariance matrixes are equal; while in Two-Dimensional Heteroscedastic Discriminant Analysis (2DHDA), within-class covariance matrixes are heteroscedastic. Based on the characters of 2DLDA and 2DHDA, Weighted Two-Dimensional Heteroscedastic Discriminant Analysis (W2DHDA) is introduced and used in face recognition,...
In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented for face recognition. In 2DHDA, small sample size problem (S3 problem) of Heteroscedastic Discriminant Analysis (HAD) is overcome. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, criterion of 2DHDA, log and rearranging terms are taken, and then...
Face recognition has been of interest to a growing number of researchers, and many algorithms are presented. However, the recognition rate will be significantly reduced in the case of large sample size and greater facial expression changes. In this paper, 2DPCA algorithm is used for features extraction and Boosting by filtering method is used to choose training samples. Then, the expert systems of...
This paper presents a novel algorithm-kernel based 2D symmetrical principal component analysis (K2DSPCA), which takes full advantage of kernel method, the symmetrical property of facial image and the structural information of image (i.e., the advantage of two-dimensional PCA). Firstly, a facial image is decomposed into an even image and an odd image; Secondly, both the even image and the odd image...
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