Adaptive Principle Component Analysis to Improve Scale Invariant Feature Transform Matching for Face Recognition Applications
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چکیده
Image matching using feature extraction is an important issue in computer vision tasks. The main drawback of matching process is the bottleneck problem that rapidly appeared when the number of features increased. This paper produced an adaptive approach to improve Scale Invariant Feature Transform (SIFT) matching. The main idea is to increase the number of SIFT points by using Adaptive PCA in wavelet domain. In this paper, the eigenface of PCA will entered to SIFT algorithm for feature matching, and thus only the SIFT features that belong to specific clusters are matched according to identified threshold. The performance of the adaptive method is implemented on AT&T ORL database. The APCA extends SIFT features matching in face images with its corresponding eigenfaces when compared with the result of the original PCA eigenfaces that entered to SIFT. As a result, the using of PCA in wavelet domain minimizes the size of the face image that entered to SIFT, this leads to increase the number of keypoints in face image and allowed to get best matching result, In addition to the ease of the adaptive method implementation.
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