Multi-view Face Expression Recognition—A Hybrid Method

摘要

Facial expressions play a significant role in human communication, and their automatic recognition has several applications especially in human–computer interaction. Recognizing facial actions is very challenging due to uneven facial deformation, angle of face poses, ambiguous and uncertain face component measurements. This paper proposes a hybrid approach for face pose detection and facial expression recognition. To speed up expression evaluation process, pose estimation is carried out prior to feature extraction to select appropriate shape model. The major contribution of this paper is introducing a hybrid classification method which uses Ada-Boost for Action Unit classification and Temporal Rule-based classification for correcting Action Unit errors. The experimental results show that this hybrid classification method produces better performance than other classifier which ideally improves overall performance of the system.