Study On Recognition Techniques Of Facial Expression

Recent advances in image analysis and pattern recognition open up the possibility of automatic detection and classification of emotional and conversational facial signals. Automating facial expression analysis could bring facial expressions into man-machine interaction as a new modality and make the interaction tighter and more efficient.Our aim is to explore the issues in design and implementation of a system that could perform automated facial expression analysis. In general, three main steps can be distinguished in tackling the problem. First, before a facial expression can be analyzed, the face must be detected in a scene. Based on color transformation and a novel lighting compensation technique, skin regions can be detected over the entire image; then eye and mouth maps are constructed for verifying each face candidates. The second step is to devise mechanisms for extracting the facial expression information from the observed facial image. Images are transformed using a multi-scale, multi-orientation set of Gabor filters. Since Gabor vectors at neighboring pixels are highly correlated and redundant, a rectangular grid is then automatically registered with the face based on the result of face detection. The amplitude of the complex valued Gabor transform coefficients are sampled on the grid and combined into a single vector. After extracting facial expression information, the final step is to define some set of categories, which are used for facial expression classification and /or facial expression interpretation, and to devise the mechanism of categorization. According to the utilized face database, three facial expression categories are defined: neutral, happiness and anger. The categorization architecture is based on a SOM. In order to eliminate influence of initial values and sequence of input examples in SOM, supervised learning is introduced into the training stage. By three steps mentioned above, theautomated classification of facial expression is realized.In this paper, an automatically facial expression analysis system is constructed based on the image processing and pattern recognition. Experiments show good classification result can be obtained through our system.