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Robust real time face tracking in mobile devices

In this paper, we propose an efficient algorithm for face tracking on mobile platforms. First we make an improvement over the robust mean shift tracking by introducing a new type of features, named First Class Feature Point (FCFP). And it handles normal tracking condition due to accuracy, computational cost and robustness. Secondly, in order to deal with a fast or a sudden movement of face, we combine the meanshift concept with particle filter by focusing on the localization and computational cost. In order to switch the tracking methods, that are, from improved robust meanshift tracking (IRMT) to the combination of particle tacking and meanshift (CPFMSH) and vice versa, we propose two approaches: using the difference of histograms and the velocity of the smartphone. Moreover, we compare our approach with several algorithms with several challenging video sequences. At the end, we show how experimental results of our approach can track the face very robustly, accurately and more importantly without much computational cost, with times between 6 and 20ms with respect to face size.