分块PCA与最大散度差鉴别分析结合的人脸识别

提出了一种将分块PCA与最大散度差鉴别分析相结合的人脸识别方法。该方法是先对原始的人脸图像进行分块，然后对分块得到的子图像矩阵采用PCA方法进行特征抽取，从而把原始模式从高维空间映射到较低维空间。接下来再对新模式采用最大散度差线性鉴别分析，这样就避免了对新模式的类内散布矩阵非奇异的要求。在ORL人脸库和Yale人脸库上分别检验了分块PCA与最大散度差鉴别分析相结合的人脸识别方法的识别性能，实验结果表明该方法抽取的鉴别特征有较强的鉴别能力。

Combination of modular PCA and maximum scatter difference discriminate analysis for face recognition

In this paper,a new method of combination of modular PCA and Maximum Scatter Difference Discriminate Analysis(MSDDA) is developed.In the proposed method,the original face images are divided into smaller sub-images.Then the PCA ap-proach is applied to each of these sub-images,and the new lower dimensionality patterns take the place of the original patterns.Because the MSDDA eliminates the redundant information within the features,in the end,the MSDDA is performed for the pat-tern classification.Finally,extensive experiments performed on both ORL face database and Yale face database verify the effective-ness of the proposed method.