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Mastering Occlusions by Using Intelligent Facial Frameworks Based on the Propagation of Movement

In uncontrolled settings occlusions occur and interfere with facial expressions recognition task. It is interesting to limit the number of regions required for face expression recognition task in order to moderate the occlusion interference. We propose a weighting scheme that ranks the facial regions needed to recognize expressions. Weights are calculated based on the contribution of each region to boost recognition in presence of various occlusions. Intelligent facial frameworks, based on region ranks are computed in presence of static occlusions (such as glasses, hair, hand on the face). Evaluations conducted using motion information as the underlying descriptor show that our approach maintains, per expression, very good recognition rates under various static occlusions occurring in uncontrolled settings.