In this paper, we propose a new facial expression recognition approach based on the use of Gabor filter features. These features are extracted only from regions of interest (ROI). These regions are the components of the face most involved in the expression of emotions. Then we select the best features with PCA. Finally, the SVM classifier is used for the classification of basic facial expressions. The experiment results show the superiority of the method and researched recognition rates of 95.11% and 92.19% for JAFFE and CK+ datasets respectively.
Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.