Our goal is to automatically determine the cast of a feature-length film. This is challenging because the cast size is not known, with appearance changes of faces caused by extrinsic imaging factors (illumination, pose, expression) often greater than due to differing identities. The main contribution of this paper is an algorithm for clustering over face appearance manifolds. Specifically: (i) we develop a novel algorithm for exploiting coherence of dissimilarities between manifolds, (ii) we show how to estimate the optimal dataset-specific discriminant manifold starting from a generic one, and (iii) we describe a fully automatic, practical system based on the proposed algorithm. The performance of the system is evaluated on well-known featurelength films and situation comedies on which it is shown to produce good results. 1.

Recent advances have shown that clothing appearance provides important features for person re-identification and retrieval in surveillance and multimedia data. However, the regions from which such features are extracted are usually only very crudely segmented, due to the difficulty of segmenting highly articulated entities such as persons. In order to overcome the problem of unconstrained poses, we propose a segmentation approach based on a large number of part detectors. Our approach is able to separately segment a person’s upper and lower clothing regions, taking into account the person’s body pose. We evaluate our approach on the task of character retrieval on a new challenging data set and present promising results. 1.