In this paper, we propose a novel approach for facial expression analysis and recognition. The proposed approach relies on the distance vectors retrieved from 3D distribution of facial feature points to classify universal facial expressions. Neural network architecture is employed as a classifier to recognize the facial expressions from a distance vector obtained from 3D facial feature locations. Facial expressions such as anger, sadness, surprise, joy, disgust, fear and neutral are successfully recognized with an average recognition rate of 91.3%. The highest recognition rate reaches to 98.3% in the recognition of surprise. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Soyel, H., & Demirel, H. (2007). Facial expression recognition using 3D facial feature distances. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4633 LNCS, pp. 831–838). Springer Verlag. https://doi.org/10.1007/978-3-540-74260-9_74
Mendeley helps you to discover research relevant for your work.