High Speed Face Recognition Based on Discrete Cosi
2016-08-23
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%High information redundancy and correlation in face images result in
% ineciencies when such images are used directly for recognition. In
% this paper, discrete cosine transforms are used to reduce image
% information redundancy because only a subset of the transform
% coecients are necessary to preserve the most important facial
% features such as hair outline, eyes and mouth. We demonstrate
% experimentally that when DCT coecients are fed into a backpropagation
% neural network for classi cation, a high recognition rate can be
% achieved by using a very small proportion of transform coecients.
% This makes DCT-based face recognition much faster than other
% approaches. Key words: Face recognition, neural networks, feature
% ineciencies when such images are used directly for recognition. In
% this paper, discrete cosine transforms are used to reduce image
% information redundancy because only a subset of the transform
% coecients are necessary to preserve the most important facial
% features such as hair outline, eyes and mouth. We demonstrate
% experimentally that when DCT coecients are fed into a backpropagation
% neural network for classi cation, a high recognition rate can be
% achieved by using a very small proportion of transform coecients.
% This makes DCT-based face recognition much faster than other
% approaches. Key words: Face recognition, neural networks, feature
% extraction, discrete cosine transform.
matlab
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