| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 95 === Face recognition has received much attention during the past several years. Principal component analysis (PCA) is one of the most successful methods for face recognition but it is not highly accurate when the illumination and pose of the facial images vary considerably. Many researches have discussed some solutions to solve the illumination and pose problems, but most of them need multiple training images. This paper presents a novel face recognition system based on PCA, named Automatic Pose normalization and Edge map face Recognizer (APER). The idea is to automatically re-render a pose invariant reference model to accommodate varying pose of the images. Face edge images, which are insensitive to illumination changes, are incorporated. The APER requires only a single face image for training per person. The APER and the PCA method are evaluated using ORL database. The experimental results demonstrate that the APER can improve the performance of conventional PCA approach under varying pose and illumination with single training image.
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