Woo Yeon Han , Mi Young Nam , Phill Kyu Rhee

The KIPS Transactions:PartB , Vol. 12, No. 5, pp. 577-586, Oct. 2005
10.3745/KIPSTB.2005.12.5.577,   PDF Download:

Abstract

Many algorithms have been proposed for face 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 this 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.

Statistics
Cite this article