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An optimization of the K-Nearest Neighbor using Dynamic Time Warping as a measurement similarity for facial expressions recognition

At the present time, the facial expressions recognition system from video sequences is a huge challenging problem to face in human and computer interaction. Emotions are important information to retrieve and treat. With the developed system, we were able to decode facial expressions and deduce human emotions, all in keeping a low error rate. The aim of this paper is to bring together two areas of which are K-Nearest Neighbor (KNN) and Dynamic Time Warping (DTW) applying for facial expressions classification. The proposed system constructs the robust features based on Active Shape Models (ASM), computes distance similarity between feature vectors by DTW, then uses KNN as a classifier. Our system achieved 90.1% of recognition accuracy using the Cohn-Kanade (CK+) facial expressions database.