Fusing cluster-centric feature similarities for face recognition in video sequences

• We present a new dual-feature approach to face recognition in video.
• Subspace and point features within local appearance-based clusters are utilized.
• Relevant similarity metrics are formulated to model a Bayesian MAP classifier.
• We report promising results based on extensive evaluation on face video datasets.

The emergence of video has presented new challenges to the problem of face recognition. Most of the existing methods are focused towards the use of either representative exemplars or image sets to summarize videos. There is little work as to how they can be combined effectively to harness their individual strengths. In this paper, we investigate a new dual-feature approach to face recognition in video sequences that unifies feature similarities derived within local appearance-based clusters. Relevant similarity matching involving exemplar points and cluster subspaces are comprehensively modeled within a Bayesian maximum-a posteriori (MAP) classification framework. An extensive performance evaluation of the proposed method on three face video datasets have demonstrated promising results.