This paper presents an approach to increase the robustness of
Active Appearance Models
(AAMs) within the scope of human-robot-interaction. Due to unknown environments with changing illumination conditions and different users, which may perform unpredictable head movements, standard AAMs suffer from a lack of robustness. Therefore, this paper introduces several methods to increase the robustness of AAMs. In detail, we optimize the shape model to certain applications by using genetic algorithms. Furthermore, a modified retinex-filter to reduce the influence of illumination is presented. These approaches are finally combined with an adaptive parameter fitting approach, which can handle bad initializations. We obtain very promising results of experiments evaluating the IMM face database .
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