| Summary: | 碩士 === 銘傳大學 === 電腦與通訊工程學系碩士班 === 103 === This thesis presents a face recognition system based on keypoint exclusion and dual keypoiont detection. There are three major problems with conventional SIFT (Scale Invariant Feature Transform). (1) It uses single type keypoint detector. For images of small size the number of detected keypoints may be too small and this causes difficulties on image matching. (2) Each keypoint of the test image is matched independently against all keypoints of the training images. This is very time consuming. (3) Only similarities between descriptors are compared and this may still causes some false matches.
To increase the number of keypoints, SIFT and FAST (Features from accelerated segment test) keypoints are combined for face image matching. Since there is no corresponding descriptor for FAST detector, the LOG (Laplace of Gaussian) function with Automatic Scale Selection is applied on each FAST keypoint to find proper scales and corresponding SIFT descriptors. On the other hand, based on the similarities between locations of features on human faces, three keypoint exclusion methods (relative location, orientation, and scale) are proposed to eliminate impossible keypoints for further descriptor matching. In this way, the number of false matches can be reduced and hence higher recognition rates can be obtained. On the other hand, matching time can also be reduced.
The proposed algorithms are evaluated with the ORL and the Yale face databases. Each database pick 10 person, every person get 10 image. Our proposed method shows significantly improvements on recognition rates over conventional methods.
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