A unified stochastic model for detecting and tracking faces

We propose merging face detection and face tracking into a single probabilistic framework. The motivation stems from a broader project in algorithmic modeling, centered on the design and analysis of the online computational process in visual recognition. Detection is represented as a tree-structured graphical network in which likelihoods are assigned to each history or "trace" of processing, thereby introducing a new probabilistic component into coarse-to-fine search strategies. When embedded within a temporal Markov framework, the resulting tracking system yields encouraging results.