| Summary: | 碩士 === 國立中興大學 === 資訊科學與工程學系所 === 98 === Face recognition is an important but complex problem which has been widely used in many fields, such as surveillance, security and telecommunications. The complexities of face recognition mainly lie in the changing appearance of human face, such as variations in illumination, posture and expression. To overcome the recognition problem of faces with varying expression and posture, the PCA-base face recognition had presented to solve it. Generally, the PCA-based method is utilized in the feature extraction process to reduce the dimensionality of the original face images. However, it has the following problems: (1) the extracted features are global features for all face classes and thus may not be optimal for discriminating one face class from the others; (2) the computation of principal components are data dependent; (3) it does not work well for recognizing human faces under different illumination.
A new feature extraction method for face classification is presented in this study. Wavelet Transform is used to remove noise-like pixel from the face images; it eliminates the effect of noise. In order to speed up the classification, (2D)2PCA method is performed for feature reduction. The extracted features using the proposed method are robust to varying illumination, different posture and expression changes. Then picking important features by feature selection method; it efficient reduces the number of features and improve the performance. Finally, nearest-neighbors based classifier is used for face recognition. Expected experimental result show that theproposed method achieves satisfactory classification result.
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