Research And Improvement On Facial Feature Points Localization Using Active Appearance Model

Automatic Face Recognition (AFR) mainly studies on how to give a computer the capability of recognizing identification through people faces. As a research field of pattern recognition, it not only has very important scientific significance, but also has great commercial application values. After decades of development, AFR has reached a very high recognition rate in the controlled environment and artificial situation, lots of business systems applied practically. There are three main parts of an AFR system, face detection, facial feature points positioning, and facial feature extraction and feature classification. As the most important part of AFR system, the accuracy of facial feature points positioning extremely affects AFR systems' performance, meanwhile it is the key step of facial expression recognition and 3D face modeling. Active appearance model (AAM), as one of the most important and efficient methods of facial feature points positioning, has been studied and applied to actual AFR systems by many scholars. In this paper, the problems of the method in practical application are encountered in the deep analysis and researches, the corresponding methods for its lack are pointed out as well.There are three main issues for improvement: 1) In terms of the accuracy of AAM under varied illumination, a fast Gabor wavelet algorithm is proposed to compute AAM Fitting and Texture Modeling; 2) For the problem of occlusion, a sub-block weighted AAM fitting algorithm is present, which first divides a region is into sub-blocks with different weights according to the proportion of occlusion. The weights of each sub-block are adjusted in the fitting process in order to eliminate occlusion influence; 3) To enhance the speed of the AAM fitting, a multi scale fitting strategy is put forward. Because lower scale image has less texture information, the speed of fitting is fast. Through the lower scale fitting which provides a better initial location, the fitting on high scale image is accelerated.Finally, we design a demo system of face detection using AdaBoost and facial feature localization using the improved AAM proposed in this paper. The system implemented under Visual Studio 2005 by appropriate designing its functional modules.