Facial Emotion Recognition System Based on PCA and Gradient Features

An efficient framework is proposed to deal with the facial emotions recognition problem. Since facial expressions result from facial muscle deformations, gradient features are exceptionally sensitive to the object deformations, so apply the gradients to encode these facial components as features. Then further it is joined by the testing process that classifies emotions and results are measured in terms of false acceptance rate, false rejection rate, and recognition accuracy. Proposed system was trained using random forest classifier to recognize the facial emotions. Japanese Female Facial Emotion (JAFFE) database consist of 5 typical emotions, namely, sad, happy, angry, neutral and surprise is considered for experimental results. Proposed framework can be used in real life applications like electroencephalogram in collaboration with brain computer interfaces. The average classification rate on the JAFEE dataset reaches 91.3%. In the proposed system hybridization of Gradient filter, PCA and PSO has been done for facial emotion recognition which has never been used earlier and this hybridization produces performs better than already existing techniques. Experimental results demonstrate the competitive classification accuracy of our proposed method.