The Research Of Facial Expression Recognition Algorithms

As an important part of the face detection technology, facial expression recognition is a new research topic in the field of artificial intelligence. Facial expression recognition is an interdisciplinary topic since it related to computational intelligence, pattern recognition, image processing, even physiology and psychology and so on. The goals of expression recognition are to enable computer to recognize facial expression information automatically, and further enhance the friendliness and intelligence of man-machine interaction. At the same time, along with the improvement of living standard, the security requirements of people’s living qualities are gaining higher and higher attention. Considering many scenes in real life, such as driving monitor and medical care and so on, if computer is capable of recognizing facial expression automatically, the possibility of tragic events can be greatly reduced and effective protection can be provided for the human safety. So the facial expression recognition research has high potential application values and broad application prospects.Now the technology of the frontal face detection is approaching mature, but the technology of facial expression recognition as an extension of the face detection is in its infancy, which still do not has a relatively mature algorithm. The current facial feature extraction algorithm can be divided into two categories:static images based and image sequences based. The classic LBP (Local Binary Patterns) and LBP_TOP (Local Binary Patterns from Three Orthogonal Planes) are two most commonly used algorithms which belong to the above mentioned categories respectively. However, their computation time and the recognition rates are not satisfactory. Based on these two classic algorithms, we modified the expression region selection method. As a result, the expression features extraction speed was greatly improved without reducing the recognition rate. In addition, for the geometric feature extraction, we propose a chain code based method which can extract the geometric feature robustly for both static images and image sequences. By combining the geometric features with the improved LBP and LBP_TOP expression features effectively, the expression recognition rates can be greatly improved. Experimental results showed the reasonability and validity of proposed geometric features extraction method. Finally, a real time facial expression recognition system which combining the features extracted from static and sequence image was built. Below is the detailed research content and innovation of this thesis:(1)As the preprocessing step, the face detection, facial feature points positioning and facial images normalization for static and sequence images was performed, this laid solid foundation for the following feature extraction procedure.(2)Based on the idea of chain coding, the geometrical features of static image were obtained by key feature points circularly chain coding, sequential combining and normalizing. Based on the classical LBP facial expression feature extraction algorithms, we decrease the dimension of LBP facial expression feature effectively by modifying the facial expression region selection method. The final static image facial expression features were obtained by effectively combining geometrical features with improved LBP features.(3) For image sequences, the movement pattern of key feature points was obtained by analyzing the position of the corresponding key feature points. The combination of these movement patterns can describe the formation of different facial expressions. The geometrical features of image sequence can be obtained by normalizing the non-circularly chain coding and orderly combining the movement patterns. The final facial expression feature is a combination of geometrical features which extracted from image sequence and the classical LBP_TOP features.(4) Using support vector machine which use the’one to one’classification algorithm and RBF kernel function to perform the expression features template training of expression classification.(5)A real-time facial expression recognition system was developed. This system constitutes the following functions:image sequences extraction, face image preprocessing, facial expression features extraction, and facial expression classification. In order to get a more reasonable result, the maximum probability inference was performed on the results obtained from static and sequential images by taking the features extracted from image sequences as the main judging factor, and the features extracted from static images as a lesser judging factor.