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http://dx.doi.org/10.3745/KIPSTB.2005.12B.5.577

Clustering Technique Using Relevance of Data and Applied Algorithms  

Han Woo-Yeon (인하대학교 컴퓨터 정보공학과)
Nam Mi-Young (인하대학교 컴퓨터 정보공학과)
Rhee PhillKyu (인하대학교 컴퓨터 정보공학과)
Publication Information
The KIPS Transactions:PartB / v.12B, no.5, 2005 , pp. 577-586 More about this Journal
Abstract
Many algorithms have been proposed for (ace recognition that is one of the most successful applications in image processing, pattern recognition and computer vision fields. Research for what kind of attribute of face that make harder or easier recognizing the target is going on recently. In flus paper, we propose method to improve recognition performance using relevance of face data and applied algorithms, because recognition performance of each algorithm according to facial attribute(illumination and expression) is change. In the experiment, we use n-tuple classifier, PCA and Gabor wavelet as recognition algorithm. And we propose three vectorization methods. First of all, we estimate the fitnesses of three recognition algorithms about each cluster after clustering the test data using k-means algorithm then we compose new clusters by integrating clusters that select same algorithm. We estimate similarity about a new cluster of test data and then we recognize the target using the nearest cluster. As a result, we can observe that the recognition performance has improved than the performance by a single algorithm without clustering.
Keywords
Face Recognition; Pattern Recognition; Computer Vision; Clutering;
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Times Cited By KSCI : 2  (Citation Analysis)
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