Facial Expression Recognition Based on Facial Components Detection and HOG Features

In this paper, an effective method is proposed to handle the facial expression recognition problem. The system detects the face and facial components including eyes, brows and mouths. Since facial expressions result from facial muscle movements or deformations, and Histogram of Oriented Gradients (HOG) is very sensitive to the object deformations, we apply the HOG to encode these facial components as features. A linear SVM is then trained to perform the facial expression classification. We evaluate our proposed method on the JAFFE dataset and an extended Cohn-Kanade dataset. The average classification rate on the two datasets reaches 94.3% and 88.7%, respectively. Experimental results demonstrate the competitive classification accuracy of our proposed method. Keywords—facial expression recognition, HOG features, facial component detection, SVM

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