Abstract
Mental condition and sentiment of a person can be analyzed through facial expressions. An emotion recognition system is proposed by recognizing facial expressions. Input images are preprocessed and then proposed image segmentation method is applied to segment a facial image into four parts that contribute highly in representing facial expressions. Features are extracted from the segmented parts using a fusion of Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP). The dimension of the feature vector is reduced using Principal Component Analysis (PCA). Finally, Artificial Neural Network (ANN) is used to classify the facial expressions properly. The proposed system is tested using three widely used facial expression datasets (JAFFE, CK +, RaFD). At last, the achieved performance is compared with other facial expression recognition systems to justify that the proposed method succeeds in achieving state-of-the-art performance.
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