人脸识别是模式识别领域中一个相当困难而又有理论意义和实际价值的研究课题.传统的基于K-L变换的自动人脸识别方法,不用过多地考虑人脸的局部特征,利用特征脸方法进行识别,取得了一定的进展.但是,人脸作为一个特殊的场景,脸像会受年龄、心情、拍摄角度、光照条件、发饰等因素影响,所成图像存在差异.传统的基于K-L变换的自动人脸识别方法不能很好地克服这些畸变的影响.文中将主成分分析方法引入人脸识别,模拟人脸脸像的各种变化,事先对脸像做相应的变化,产生一系列变形脸.然后对变形脸进行主成分分析,提取它们的主成??最后应用遗传算法选择最优特征向量构造子空间,提出一种能抗御一定脸像变化的人脸识别方法,并运用该方法进行了实验.实验结果证明了该方法的可行性和良好的抗畸变能力.

Human face recognition is one of the relative difficult and important issues in the field of pattern recognition, which is significant both in theoretical research and in practical application. Traditional methods of face recognition based on K-L transform do not necessarily correspond to isolated features such as eyes, ears, and noses. And face recognition using eigenfaces has made much progress. But for complex and special scenes, facial images will change as a result of the effect of age, emotion, illumination changes, and hair, so the images of the same person differ drastically. Traditional methods of automatic face recognition based on K-L transform can not cope with such effects. In this paper, principal component analysis is introduced into face recognition. Simulating the real changes of facial images and making corresponding distortions in advance, a series of distorted facial images are produced. Then the principal components of distorted faces are extracted with principal component analysis. Finally appropriate principal components are selected with genetic algorithm to span an eigenspace for recognition.A new distortion invariant method of automatic face recognition with learning function is proposed and experiments with this method are made.Experimental results show the feasibility and robustness of this algorithm.