非约束环境下，光照、姿态、表情、遮挡等复杂背景因素给人脸识别带来严重影响。提出一种基于AAM（active appearance model）的图像对齐和局部匹配人脸识别算法，使之能够增强人脸识别算法对姿态、表情变化的鲁棒性。AAM能够快速准确地定位人脸的特征点，进而将图像扭转到一个标准正面人脸模型中。接着，提出一种新的基于信息熵的Gabor jet加权方法用于提高人脸识别率；并且对Borda count分类器组合方法进行了改进，认为在投票过程中为其设置阈值来排除“噪声”的干扰可以提高识别率。通过与多种人脸识别方法的实验结果比较表明，使用AAM矫正图像后，联合熵加权Gabor方法和加阈值Borda能够取得比单独使用更好的成绩。

In unconstrained environments,face recognition results can be seriously affected by inner and outer factors such as expression,attitude,light conditions and background. In this paper,we mainly study the image alignment based AAM (active appearance model) and local matching approach for face recognition that will be able to enhance the robustness to the change of attitude and expression.AAM can rapidly and accurately locate facial feature points,and then warp the picture into a "standard positive" face model.Several models based on Gabor feature have been proposed for face recognition with very good results on available face databases. In this paper,a methodological improvement on Gabor features is proposed and used to align face data by AAM. We select and weight Gabor jets by entropy measure.Then, we bring a threshold to the Borda count classification,eliminating low score jets produced by the voting and consequently,increasing the face recognition rate. Experiments indicate that combination of weighting Gabor jets features with Borda count thresholding can yield the perfect results on face data aligned by AAM.