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Face Detection Based on Gabor Wavelet Transform and Statistical Cluster Model

A gray-image face detection algorithm was proposed, in which feature vectors were Gabor wavelet coefficients at 4 scales and 6 orientations. Firstly, feature vectors of training samples and negative samples were separately clustered. Secondly, these clusters were reduced by discriminate analysis. Finally, distribution model of feature vectors was built and average probability was calculated to determine if the testing image was similar to face samples. The experimental results show the algorithm is effective.