Face likelihood functions for visual tracking in intelligent spaces

The Viola and Jones face detectors and Particle Filters are great algorithms for face detections and target tracking. However Viola outputs a binary result, while Particle Filters work with probabilistic inputs. This is the reason why there are not so many works that combine both algorithms. A probabilistic model or likelihood functions to transform Viola and Jones output to probabilistic data are needed to allow linking both methods. In this work we explore some Viola and Jones based likelihood functions presented in literature, and propose new strategies. We also extend the evaluation of the likelihood functions in position, scale and pose. One of our proposed functions shows better characteristics to be used in intelligent spaces in three dimensional face tracking applications.