FaceHugger: The ALIEN Tracker Applied to Faces

Abstract

This paper proposes an online tracking method which has been inspired by studying the effects of Scale Invariant Feature Transform (SIFT) when applied to objects assumed to be flat even though they are not. The consequent deviation from flatness induces nuisance factors that act on the feature representation in a manner for which no general local invariants can be computed, such as in the case of occlusion, sensor quantization and casting shadows. However, if features are over-represented, they can provide the necessary information to build online, a robust object/context discriminative classifier. This is achieved based on weakly aligned multiple instance local features in a sense that will be made clear in the rest of this paper. According to this observation, we present a non parametric online tracking by detection approach that yields state of the art performance. Specific tests on video sequences of faces show excellent long-term tracking performance in unconstrained videos.