Natural facial expression recognition using differentialAAM with manifold learning

This paper proposes a novel natural facial expression recognition method that recognizes a sequence of dynamic facial expression images using the differential active ap-pearance 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 in-put face image and a reference face image. Second, we per-form the manifold learning. Third, we recognize the facial expression of the input face image in the embedded fea-ture 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 conven-tional AAM features by 20 % and (2) the sequence-based k-nearest neighbors classifier provides a 95 % of facial ex-pression recognition performance on the facial expression database (FED06). 1