Facial expression recognition for learning status analysis
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: users and applications - Volume Part IV
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This paper proposes a novel natural facial expression recognition method that recognizes a sequence of dynamic facial expression images using the differential active appearance model (AAM) and k-NNS as follows. First, we use the differential-AAM features (DAFs) that are computed from the difference of the AAM parameters between an input face image and a reference face image. Second, we perform the manifold learning. Third, we recognize the facial expression of the input face image in the embedded feature space using sequence based k-NN, k-NNS. Since we use DAFs, we also propose an effective way of finding the neutral facial expression as kernel density approximation. Experimental results show that (1) the DAFs improves the facial expression recognition performance than the conventional AAM features by 20% and (2) the sequence-based k-nearest neighbors classifier provides a 95% of facial expression recognition performance on the facial expression database (FED06).