In many domains of computer vision, such as medical imaging and facial image analysis, it is necessary to combine shape (geometric) and appearance (texture) information. In this paper, we describe a method for combining geometric and texture-based evidence for facial actions within a Kalman filter framework. The geometric evidence is provided by a face alignment method. The texture-based evidence is provided by a set of Support Vector Machines (SVM) for various Action Units (AU). The proposed method is a practical solution to the problem of fusing categorical probabilities within a Kalman filter based state estimation framework. A first performance evaluation on upper face AUs demonstrates the practical applicability of the proposed fusion method. The method is applicable to arbitrary imaging domains, apart from facial action estimation.
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