| Summary: | 碩士 === 義守大學 === 電機工程學系 === 91 === The methods of feature extraction are most importance in a face recognition system. There are two representation methods, template matching and geometric feature. In the 2D face recognition, the recognition rate is depended on the illumination, face location and viewing direction. The gray-value in the 2D face image is due to the illuminated intensity. This thesis studies the face recognition using the 3D faces which are reconstructed by Photometric Stereo Method. Our images contain with the depth-information which reconstructed in 3D faces. They are different to 2D gray-value images. We know that Principal Component Analysis method has better performance in face recognition. And it had been applied in computer vision, industrial robotics. In this thesis, we will present the novel approach which combines wavelet and PCA to generate the face feature. Furthermore, we will employ this face feature to compare with both in wavelet space and PCA space for face recognition, and compare the results. Our results will show the comparison results of different classifiers, for example, Nearest Center, Nearest Feature Line and Linear Discriminant Analysis, we will show that this new approach reveals the more excellent performance in face recognition.
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