Video face recognition with graph-based semi-supervised learning
We consider the problem of classification of multiple observations of the same object, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all unlabelled examples belong to the same unknown class. We propose a low complexity solution that is able to exploit the properties of the data manifold with a graph-based algorithm. It results into a discrete optimization problem, which can be solved by an efficient algorithm. We demonstrate its performance in video-based face recognition applications, where it outperforms state-of-the-art solutions that fall short of exploiting the manifold structure of the face image data sets.