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 PCA 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 PCA 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.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第26期216-218,共3页
Computer Engineering and Applications
基金
国家教育部科学技术研究重点项目(No.208074)
济宁学院科研基金项目(No.2009KJLX04)
关键词
主成分分析
模块主成分分析
特征抽取
人脸识别
Principal Component Analysis（PCA）
modular principal component analysis
feature extraction
face recognition
