KNN分类算法实现人脸识别，数据集为ORL。训练样本分别为2、4、6，其余为测试样本。-KNN classification algorithm for face recognition, the data set for the ORL. 2,4,6 training samples respectively, the rest of the test samples.

PCA主成分分析，采用KL投影和SVD分解提取人脸特征向量，最后采用最近邻判别法计算识别率。-Face recognition based on PCA. KL projection and SVD are used to extract face eigenvectors. Recognition rate is calculated by k nearest neighbors(KNN) method.

可以分别介绍MFA，又名边界Fisher分析。对模式识别十分重要的作用，可以帮助新手更好的理解KNN原理，对人脸识别有着很好的演示作用。-Can introduce MFA respectively, also known as boundary Fisher analysis. For pattern recognition is an important role, can help beginners a better understanding of KNN principle, has

用PCA算法和KNN算法实现人脸识别，参数可以自己调整(The PCA algorithm and the KNN algorithm are used to realize the face recognition, and the parameters can be adjusted by themselves)