Facial expression Recognition based on Motion Estimation
Hemir da Cunha Santiago, Tsang Ing Ren, George D. C. Cavalcanti · 2016
In this paper, we propose a novel facial expression recognition method based on features of the motion, Facial Expression Recognition based on Motion Estimation (FERME). The proposed approach encodes the directional information of the facial expression. The facial motion is encoded by using the motion estimation between different images from the same (or similar) face. The facial expression image is compared against the most similar image from each facial expression of training database. The best match is obtained using the Structural Similarity Index (SSIM). We propose a modified version of the Adaptive Reduction Search Area algorithm (MARSA) for motion vector calculation. FERME compares the motion vectors to the vectors of the highest occurrences obtained from each facial expression. From this comparison, the Euclidean distances vectors are generated. Support Vector Machine (SVM) is used to classify the facial expression. The experimental results show the effectiveness of the proposed approach.