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(a)Sample for different poses of one subject contained in the Sheffield Face Multi-View Database used in our experiment(b)Sample for different poses of another subject contained in the Sheffield Face Multi-View Database used in our experiment
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This paper develops a new Face Recognition System which combines R-KDA for selecting optimal discriminant features with non-linear SVM for Recognition. Experiment results have been conducted showing the comparison of enhanced efficiency of our proposed system over R-KDA with k-nn as the similarity distance measure.
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... files are all in PGM format, approximately 220 x 220 pixels in 256 shades of grey. Out of these total images, 10 different poses each of 18 different subjects are taken for our experimental purpose.Samples of two different persons are given in Fig.3(a)and(b).Six images of different poses are used for training and the rest for testing. ...
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... To improve the face recognition process, the features with more differentiation power between individuals will be effective. Principal component analysis (PCA), linear discriminant analysis (LDA), and discrete cosine transform (DCT) are the most popular feature extraction Fig. 1 A general face recognition system (The block encircled by red box is a crucial step in the system) [11] methods for face recognition. PCA and LDA use training data to learn a subspace. ...
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