Research On Facial Expressions Recognition Method

Facial expression recognition technology enables the computer to recognize the hunman facial expressions and create a truly harmonious human-machine environment. Expression recognition has extraordinary significance to establish friendly man-machine interface. Nowadays, expression recognition technology has in-depth applied to many areas of our daily lives:the distance education system, driver fatigue detection system and smile detection technology. Therefore, this paper focuses on several key technologies in facial expression recognition.Expression recognition technology is different from face recognition and texture recognition; it has its own unique definitions and characteristics. Today, the expression recognition of key technologies research focus on two aspects:(1) Use the unique characteristics of facial expressions (facial action unit) to improve the classic face recognition or texture recognition method;(2) Propose facial expression recognition method by simulating the biological visual system, the method has the characteristics of the biological visual system and has a certain robustness to noise and occlusion. We improve these tecnologies by using some artificial intelligence techniques such as digital image processing and biological visual perception, based on the research of the existing advantages of key technologies,Expression recognition method based on facial action unit is one of the key technologies in the current expression recognition method. Based on the analysis of the classical algorithm, we propsed a new feature combination strategy and a final classifying method with better classification capabilities to improve the accuracy rates of expression recognition. However, the expression recognition method based on facial action unit is not considered the corruption and occlusion problems. So, we study the other key technology in facial expression recognition which can robust to occlusion.As the biological visual system is able to very easily distinguish the facial expressions with noise and occlusion, so sparse representation based classifiers (SRC) are more and more applied to facial expression recognition.Based on the research of SRC, we proposed a variety of improvement ideas:(1) Propose to use histogram of gradient descriptor to take place of traditional feature extraction method, for the purpose of increasing the accuracy rates of SRC.(2)Propose an expression recognition model by simulating biological visual based on the existing research result. Determine that the Local Binary Patterns and histogram of gradient descriptor are the best features. For the purpose of further increasing the accuracy rates, use classifier combination method which based on Bayesian theory to fuse the results of two classifier methods.(3) Propose two feature selection criterias to solve the problem of time-consuming. Select a new feature based on these two criterias, the facial expression recognition method which based on the feature and SRC can decrease the time-consuming and give better performance than the exisiting method based on SRC.(4) For the purpose of increasing the robustness of SRC, appling a robust sparse coding model to facial expression recognition. The results show that the robustness of SRC can be improved.