文件名称:dctannprotected
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High information redundancy and correlation in face images result in efficiencies 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 coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.-High information redundancy and correlation in face images result in inefficiencies 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 coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.
相关搜索: backpropagation
mouth
matlab
dctannprotected
mouth
backpropagation
neural
network
face
recognition
based
on
dct
Image
Recognition
and
Classification
dct
face
recognition
USING
DCT
FOR
FACE
RECOGNITION
USING
MATLAB
neural
network
face
recognition
usin
mouth
matlab
dctannprotected
mouth
backpropagation
neural
network
face
recognition
based
on
dct
Image
Recognition
and
Classification
dct
face
recognition
USING
DCT
FOR
FACE
RECOGNITION
USING
MATLAB
neural
network
face
recognition
usin
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