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Evaluating Facial Expressions with Different Occlusion around Image Sequence

Occlusions provide different effects on facial image. Presence of occlusion around facial image implicates the process of recognizing facial expressions. In this paper, we proposed a method for evaluating facial expressions with different occlusion around image sequence. This method is based on local facial regions, which provides better recognition rate in the presence of facial occlusions. Proposed method uses local binary pattern as a feature extractor, which extract effective features from some important parts of facial image. We further formulate Uniform-LBP to extract the most Discriminant LBP features and this feature vectors are classified using simplest classifier that is Template Matching with chi square similarity measure on Cohn-Kanade dataset.