Combination of Multiple Measurement Cues for Visual Face Tracking
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Visual face tracking is an important building block for all intelligent living and working spaces, as it is able to locate persons without any human intervention or the need for the users to carry sensors on themselves. In this paper we present a novel face tracking system built on a particle filtering framework that facilitates the use of non-linear visual measurements on the facial area. We concentrate on three different such non-linear visual measurement cues, namely object detection, foreground segmentation and colour matching. We derive robust measurement likelihoods under a unified representation scheme and fuse them into our face tracking algorithm. This algorithm is complemented with optimum selection of the particle filter’s object model and a target handling scheme. The resulting face tracking system is extensively evaluated and compared to baseline ones.
KeywordsFace tracking Visual measurements Particle filters Likelihood function Fusion
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