计算机工程2012，Vol.38Issue(10)：151-153,3.DOI:10.3969/j.issn.1000-3428.2012.10.046
改进的二维典型相关分析及其人脸识别应用
Improved Two-dimensional Canonical Correlation Analysis and Its Application in Face Recognition
摘要
Abstract
An Enhanced Two-dimensional Canonical Correlation Analysis(E-2DCCA) method is presented to solve the problem that 2DCCA requires much storage space and runtime. By making use of the spectrum representation of images, a new class-membership matrix is constructed. A modified correlation criterion fiinction is proposed from the angel of favoring classification. Two-dimensional Principal Component Analysis(2DPCA) method is used for further dimensional reduction. Experimental results on ORL and combined face databases show that the features have powerful ability of recognition.关键词
二维典型相关分析/频谱特征/类标矩阵/准则函数/特征提取/人脸识别Key words
Two-dimensional Canonical Correlation Analysis(2DCCA)/spectrum feature/class-membership matrix/criterion function/feature extraction/face recognition分类
信息技术与安全科学引用本文复制引用
刘艳艳,曹慧荣,王建国,赵宜宾..改进的二维典型相关分析及其人脸识别应用[J].计算机工程,2012,38(10):151-153,3.基金项目
中国地震局教师科研基金资助项目(20110116) （20110116）
河北省自然科学基金资助项目(A2011408006) （A2011408006）
