计算机工程与应用Issue(2)：161-164,176,5.DOI:10.3778/j.issn.1002-8331.1303-0095
改进的模块PCA人脸识别新算法
Improved modular PCA face recognition algorithm
摘要
Abstract
The traditional Principal Component Analysis(PCA)requires that training samples are in accordance with Gauss-ian distribution strictly. However, generating pictures are always influenced by illumination, facial expressions, and pos-tures. In order to solve the problem, a new modular algorithm based on PCA is proposed, which is also a guarantee of the rate of identification. The new algorithm, on the one hand, takes a blocked mode which divides pictures with a same pos-ture into one matrix, so the training sample can be closer to the Gaussian distribution. On the other hand, since the first three characteristics of the principal component are easily affected by light variation, a less than one weighting coefficient is added to reduce the effects of light in the recognition. Thus the improved PCA training matrix is no longer limited to the Gaussian distribution with the combinations of the sub-blocks and the weight coefficients, the recognition rate is improved at the same time. The numerical experiments in the ORL human face databases show that the improved algorithm is supe-rior to the traditional PCA algorithm.关键词
主成分分析/人脸识别/权重系数/改进的主成分分析(PCA)算法Key words
principal components analysis/face recognition/weight coefficient/improved Principal Component Analysis (PCA)method分类
信息技术与安全科学引用本文复制引用
赵鑫,汪维家,曾雅云,熊才伟,任彦嘉..改进的模块PCA人脸识别新算法[J].计算机工程与应用,2015,(2):161-164,176,5.基金项目
大学生创新训练项目（No.274012529）。 （）
