Person Identity Label Propagation in Stereo Videos

In this paper a novel method is introduced for propagating person identity labels on facial images extracted from stereo videos. It operates on image data with multiple representations and calculates a projection matrix that preserves locality information and a priori pairwise information, in the form of must-link and cannot-link constraints between the various data representations. The final data representation is a linear combination of the projections of all data representations. Moreover, the proposed method takes into account information
obtained through data clustering. This information is exploited during the data propagation step in two ways: to regulate the similarity strength between the projected data and to indicate which samples should be selected for label propagation initialization. The performance of the proposed Multiple Locality Preserving Projections with Cluster-based Label Propagation (MLPP-CLP) method was evaluated on facial images extracted from stereo movies. Experimental results showed that the proposed method outperforms state of the art methods.