| Summary: | 碩士 === 國立臺北科技大學 === 工業工程與管理系所 === 93 === Biometric measurements received an increasing interest for security applications in the last two decades. In particularly, face recognition has been an active research in this area. The objective of this study is to develop an effective face recognition system that extracts both 2D and 3D face features to improve the recognition performance. The proposed method derives 3D face information using a designed stereo face system. Then, it retrieves 2D and 3D face features with Principle Component Analysis (PCA) and Local Autocorrelation Coefficient (LAC) respectively. Eventually, the information of features are fused and fed into a Euclidean-distance classifier and a Backpropagation neural network for recognition.
An experiment was conducted with 100 subjects. For each subject, thirteen stereo face images were taken with different expressions. Among them, the faces with expressions one to seven are used for training, and the rest of the expressions is used for testing. For the Euclidean-distance classifier, the proposed method does not improve the recognition result by combining the features derived from PCA with LAC; however, an improvement is observed when using the Back-Propagation Neural Network. In general, BP outperforms Euclidean distance in both 2D and 3D face recognition. Furthermore, the experimental results show that the proposed method effectively improves the recognition rate by combines the 2D with 3D face information.
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