一种改进的模块PCA人脸识别新方法

摘要

提出了一种改进的模块PCA方法,即基于独立特征抽取的模块PCA方法.算法先对图像进行分块,然后对每一子块独立地进行PCA处理,求出测试样本子块与训练样本对应子块间的距离；最后将这些距离相加得到测试样本与训练样本的距离,用最近距离分类器分类.在ORL人脸库和Yale人脸库上的实验结果表明,提出的方法在识别性能上明显优于普通模块PCA方法.%An improved modular PCA( Principal Component Analysis) method, that is modular PC A method based on independence of feature extraction, is proposed.The original images are divided into sub-images in proposed approach.Then each kind of sub-images at the same position have been disposed by PC A independently, the distance between the corresponding sub-images of the test sample and the train sample can be given.Finally, the distance between the test sample and the train sample can be caculated by adding all these distances between the sub-images together, the nearest distance classification is used to distinguish each face. Experimental results on ORL face database and Yale face database indicate that the improved modular PCA is obviously superior to that of general modular PCA.