Application And Research Of The Method In The Active Apparent On The Emotion Recognition

Facial emotion is a research topic in the field of artificial psychology. It has much potential applications in theoretical research and practical application. Around the key technologies of facial emotion recognition, the paper summarizes the facial emotion research’s background and significance and introduces the current facial expression recognition development status at home and abroad. Then Analysis mainstream methodused in facial expression feature extraction and expression classification, as well as illustrate several widely used facial expression database. This paper mainly discusses the feature extraction and classification algorithm in facial expression recognition system. The concrete work is as follows.In order to extract human facial expression features more quickly and efficiently, the paper firstly pre-process face image. After detection of the human face, then locate the center of eyes using pixels’gray information in edge image. According to the distance between right and left eye, normalize image, including sizeand rotation normalization.Secondly, since ASM is accurate in locating facial contour feature point and AAM shows an excellent performance in internal key point location, Therefore, in this paper, the method of AAM based on Gabor will be used in facial expression feature extraction.Meanwhile, in order to improve the matching speed, the graphic information is added to the measure function in the place of the traditional Mahalanobis to judge whether the algorithm convergence.Thirdly, it is in line with the SVM’s advantage that facial expression recognition is a small sample of nonlinear classification problems.As a classification method developed on the basis of statistical learning theory,support vector machine is a two-class classifier. In order to realize the identification of six basic expressions,the paper constructs the expression classifier using a plurality of S VM according to combination thought.Finally, in the foundation of the algorithm, program a simple face recognition system using C++.And use the face database images as the experimental object to test the robustness and accuracy of the facial expression recognition system.In the feature extraction, using the method of AAM based Gabor than the original ASM and AAM, speed increased by23%and17%, and accuracy improved by14%and6%. In face recognition, the use SVM of one against one combination strategy, on JAFFE database image with the experiment of People associated and not associated People, the recognition rate are88.1%and92.4%. And the recognition rate of the overall system is close to the experimental test results associated with human face recognition, identifying real-time performance of the video stream within the allowable error range.