Face Recognition In Complex Environment And Its Actualization In CRM

Face recognition is a kind of practical identification technology. As a result of its unique advantages, this has been widely used in our life and become an important technology that is convenient for people's daily life.This thesis focuses on the study of face recognition technology. Firstly, the thesis studies the face image preliminary preprocessing technology; secondly, the static face recognition and dynamic face recognition are discussed.The face recognition technology is mainly used in the security field at present.In order to service for enterprises and consumers better, the thesis designs a CRM system that based on face recognition technology.The main contents of this thesis are as follows:(1) Studying the preliminary preprocessing of the face image mainly on histogram equalization, filtering technology and so on. For the recognition of dynamic facial image, the objects of recognition are video sequences. And I study wirh regard to segmentation algorithm and localization algorithm. The thesis adops to Hausdorff distance for fixing position with dynamic image.(2) Comparing of many feature extraction methods, such as the PCA method and the feature extraction based on Gabor transform.(3) Analysising PCA method and Gabor transform. The thesis studies on their advantages and disadvantages and uses PCA feature extraction method for local facial feature points and obtain PCA coefficient feature.Then face images are transformed by Gabor and get Gabor coefficients. Finally, the feature points of the waiting recognized images and the images in the face database have two characteristics of coefficients. Experiments show that the recognition rate of this method is higher. And I propose the solution method for the complex environment.(4) The thesis studies the relativity problem of dynamic face and the correlation coefficient defined algorithm and analysises the relationship between image segmentation and correlation, And using the correlation coefficient to adjust the initial probability of HMM. Finally, HMM is regarded as the classifier for the recognition of dynamic facial images system and combined with the segmentation algorithm and localization algorithm for recognition of dynamic facial images.(5) With the face recognition algorithm in this thesis, a CRM system that based on face recognition technology is designed.And this system faces banks. Firstly, the system uses the dynamic facial recognition algorithm for face detection. Secondly, the system uses static facial recognition algorithm for face recognition.