An Improved Face Clustering Method Using Weighted Graph for Matched SIFT Keypoints in Face Region

摘要

In this paper, we propose an improved face clustering method using a weighted graph-based approach. We combine two parameters as the weight of a graph to improve clustering performance. One is average similarity, which is calculated with two constraints of geometric and symmetric properties, and the other is a newly proposed parameter called the orientation matching ratio, which is calculated from orientation analysis for matched keypoints in the face region. According to the results of face clustering for several datasets, the proposed method shows improved results compared to the previous method.