Iterative Label Propagation on facial images

Abstract

In this paper a novel method is introduced for propagating person identity labels on facial images in an iterative manner. The proposed method takes into account information about the data structure, obtained through clustering. This information is exploited in two ways: to regulate the similarity strength between the data and to indicate which samples should be selected for label propagation initialization. The proposed method can also find application in label propagation on multiple graphs. The performance of the proposed Iterative Label Propagation (ILP) method was evaluated on facial images extracted from stereo movies. Experimental results showed that the proposed method outperforms state of the art methods either when only one or both video channels are used for label propagation.