Frowning expression detection based on SOBEL filter for negative emotion recognition

This paper proposes a novel method to improve happiness status by detection negative emotional status based on frowning lines on face and a new term called facial expression factor (FEF). The FEF correlates the frowning and with emotional status. The frowning lines are detected using SOBEL filter and FEF factors are calculated from selected frowning lines to know the actual emotional status. Thus the negative emotional state are detected which could help to promote the happiness further. The experiment is conducted on 10 participants. In total 40 images (including 20 neutral and 20 frowning expression) are considered for experiment. The results show that the emotional status of 8 persons out of 10 participants is recognized correctly. Further, the wrong recognition results are corrected by tuning the threshold. Hence, the results depict the recognition accuracy up to 80%. The proposed work is based on simple training which also reduces the training time cost effectively. Furthermore, the proposed method is able to detect more complex facial expression (e.g., forced smile) using FEF. The tuning of threshold makes the method more effective. Therefore, such results show its effectiveness by detecting negative emotional state to promote the happiness.