Detecting and naming actors in movies using generative appearance models

We introduce a generative model for learning person and costume specific detectors from labeled examples. We demonstrate the model on the task of localizing and nam-ing actors in long video sequences. More specifically, the actor’s head and shoulders are each represented as a con-stellation of optional color regions. Detection can proceed despite changes in view-point and partial occlusions. We explain how to learn the models from a small number of la-beled keyframes or video tracks, and how to detect novel appearances of the actors in a maximum likelihood frame-work. We present results on a challenging movie example, with 81 % recall in actor detection (coverage) and 89 % pre-cision in actor identification (naming). 1