| Summary: | 碩士 === 國立中興大學 === 電機工程學系 === 89 === Real-time face recognition systems based on inoffensive feature extraction techniques have already produced very high identification rates. Although real-time face recognition system has many advantages, real-time face recognition in an unconstrained environment is a difficult task. Many real-time human face recognition systems operate under strict imaging conditions such as controlled illumination, image size, noises, and limited facial expressions. In this thesis, we propose a line-based face recognition algorithm combined with Gabor filters to alleviate the constraints. This algorithm achieves high recognition rates for rotations both in and out of the plane, is robust to sacling, and is computationally efficient. Before the feature vector extraction, we use Gabor filters to enhance the important facial features such as the eyes, the noise, and the mouth. After that, we use a set of random straight lines to extract the feature vector of the face image. Finally, the nearest-neighbor classifier is used to classify the test face image into one of the persons in the database. In our experiment, we use ORL face database to test this line-based face recognition system. Our method achieved an average recognition rate of 99.6 % using 1.784 seconds per view in average.
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