摘要
针对二维典型相关分析(2DCCA)中类标矩阵维数较大及算法耗时过多的问题,提出一种改进的2DCCA特征提取方法。利用图像的频谱性质定义低维的类标矩阵,从有利于模式分类的角度构造出新的准则函数,采用二维主成分分析对所得特征进一步降维,得到更具分类判别能力的低维特征。在ORL和组合人脸数据库上的实验结果表明,该特征具有较好的分类能力。
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 function 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.
出处
《计算机工程》
CAS
CSCD
2012年第10期151-153,共3页
Computer Engineering
基金
中国地震局教师科研基金资助项目(20110116)
河北省自然科学基金资助项目(A2011408006)
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
Two-dimensional Canonical Correlation Analysis（2DCCA）
spectrum feature
class-membership matrix
criterion function
feature extraction
face recognition
