| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程系 === 97 === There is an urgent need for Face Detection and Recognition System (FDRS) to be used in complex situations. However, face recognition rates are often affected by complicated background, different head angles, variant face size and position. For building a Face Recognition System under real environment, this thesis aims to solve these problems and reduce the interferences occurred in the environment. The author combines gray-scale facial feature and skin color density to improve face detection yield. Also, at face identifiable region segmentation, the author uses facial feature to orientate facial region, and then modifies each with different angles. This method can efficiently exclude the dominant effects from the complex background information and the distortions via rotating angles. At database training and face recognition stage, the Principal Component Analysis method (PCA) is adopted to decompose and reduce the mass facial feature information. Moreover, the author uses the Euclidean distance of projection coefficients of facial eigen-space to determine the recognition results.
Overall, in this thesis, it combines face detection, face region normalization and face recognition to elaborate a face recognition system under complex scenarios. In real simulation, the proposed method improves face recognition rate from 73.8% up to 91.6%. Not only does the proposed method speed up the function efficiency from 1.1 fps up to 9fps but also fulfils the instant need in real settings.
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