Facial Region Segmentation Based Emotion Recognition Using Extreme Learning Machine

A framework to recognize human emotion through facial expression recognition is proposed in this paper where segmentation of the expression regions (right eye, left eye, nose, mouth) are done manually in an easy yet effective, unique manner by analyzing many facial expression images and the possible positions of the expression regions in those images. For feature extraction from the segmented parts 2D Gabor filter is used with multiple frequency and orientation. Redundant features from the extracted features are eliminated using downsampling. Finally, Extreme Learning Machine (ELM) is used to handle the classification process. For performance evaluation of the proposed method, four different datasets (JAFFE, CK+, RaFD, KDEF) have been used and impressive correct recognition rate on these four datasets indicates the capability of the proposed system to recognize human emotion through facial expression recognition of front-facing images.

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