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A face recognition method based on the discrete cosine transform (DCT) and two dimensional linear discriminant analysis (2DLDA) is presented. First, in this paper, the dimensionality of the original face image is reduced by using the DCT and the upper-left corner of the DCT matrix is selected to be the features of face image. Next, the proper feature are abstracted from the truncated DCT coefficient...
The Fisherface is one of the most successful face recognition methods, which however, cannot be directly applied to face recognition where only one sample image per person is available for training. In this paper, a method is proposed to obtain multiple training samples from a single face image by sampling, and then Fisher linear discriminant analysis (FLDA) is applied to the set of newly produced...
The two-dimensional linear discriminant analysis (2DLDA) is one of the most successful face recognition methods. However, it cannot be directly applied to the face recognition where only one sample image per person is available for training. In this paper, we present a new method based on 2DLDA to deal with the single training sample problem. The method derive a set of sub-images from a single face...
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