Facial Expression Recognition Using 3D Facial Feature Distances
Hamit Soyel, Hasan Demirel · 2008
In this chapter we have shown that probabilistic neural network classifier can be used for the 3D analysis of facial expressions without relying on all of the 84 facial features and errorprone face pose normalization stage. Face deformation as well as facial muscle contraction and expansion are important indicators for facial expression and by using only 11 facial feature points and symmetry of the human face, we are able to extract enough information from a from a face image. Our results show that 3D distance vectors based recognition outperforms facial expression recognition results compared to the results of the similar systems using 2D and 3D facial feature analysis. The average facial expression recognition rate of the proposed system reaches up to 91.3%. The quantitative results clearly suggest that the proposed approach produces encouraging results and opens a promising direction for higher rate expression analysis.