A face recognition method integrating 2DPCA and Bayesian is proposed. First, the 2DPCA method is used for recognition, and the top 10 images are selected as candidate images. Then, the candidate images and test images are decomposed by wavelet, and Bayesian face recognition is performed on the obtained high-frequency and low-frequency sub-images in parallel. The final result is obtained by weighted sorting. Experiments on the FERET face database show that compared with traditional methods, this method reduces the amount of calculation and improves the recognition rate.
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