Exploiting clustering and stereo information in label propagation on facial images

In this paper, a method for performing semiautomatic identity label annotation on facial images, obtained from monocular and stereoscopic videos is introduced. The proposed method exploits prior information for the data structure, obtained from the application of a clustering algorithm, for the selection of the facial images from which label inference should begin. Then, a sparse graph is constructed according to the Linear Neighborhood Propagation (LNP) method and, finally, label inference is performed according to an iterative update rule. In the case of stereoscopic videos, the classification decision is determined by the combined information of the left and right channels. The objective of the proposed framework is to be used by archivists for semi-automatic annotation of television content, in order to further enable journalists to directly access video shots/frames of interest.