摘要
独立成分分析在信号处理和图像处理领域已受到广泛关注.本文提出了一种基于人脸直观自然特性的新算法——对称独立成分分析.该算法在代数上基于函数分解的思想,几何上基于人脸的镜像对称.首先引入镜像变换,然后根据奇、偶分解原理,分别生成奇偶对称样本,最后分别提取各奇偶样本的独立成分.理论分析与实验证明,该算法巧妙利用了镜像样本,既扩大了样本容量,又提高了识别率.同时该算法对光照变化有一定的不敏感性.
Independent Component Analysis （ ICA ） has been extensively used in the field of signal processing and image processing. In this paper, a new algorithm called symmetrical ICA （SICA） based on facial symmetry is proposed. This algorithm is based on the theory of [unction decomposition in algebra and mirror symmetry in geometry . In this algorithm , mirror transform is firstly introduced. Then, even/odd symmetrical samples are produced based on the theory of the even/ odd decomposition principle, and the even/odd independent components are extracted from the corresponding samples respectively Both theoretical analysis and experimental results demonstrate that this algorithm not only enlarges the number of training samples , but also remarkably raises the recognition rate. Experiment results also show this algorithm is not sensitive to the illumination variation of human faces.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2006年第1期116-121,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60472060
60572034)
南京理工大学科研发展基金(No.AB96125)
关键词
独立成分分析
对称独立成分分析
人脸识别
奇偶分解
镜像变换
Independent Component Analysis, Symmetrical Independent Component Analysis, Face Recognition, Even/Odd Decomposition, Mirror Transform
