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基于改进KCCA的快速特征提取方法 被引量：1

Fast feature extraction method based on improved KCCA
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摘要 KCCA特征提取技术具有处理非线性数据的良好性能,但是存在计算量大、特征提取缓慢的局限性。针对KCCA的这一缺点,在研究KCCA特征提取技术和SVDD分类理论的基础上,提出了一种基于改进KCCA的快速特征提取方法,并将改进后的KCCA与SVDD的优势相结合应用于人脸识别中。通过在ORL人脸库上的实验仿真和对比结果验证了所提出方法的有效性。 The latest feature extraction methods based on KCCA are introduced.However,feature extraction for one sample requires that kernel functions between training samples and the sample be calculated in advanced.In order to upgrade the extraction efficiency,an improved algorithm is developed.The framework of KCCA and SVDD used in face recognition is proposed for the end.The experiment results on the ORL face image database demonstrate the competitiveness of the method proposed.
作者 任学惠 郝元宏 李明
机构地区 兰州理工大学计算机与通信学院
出处 《计算机工程与设计》 CSCD 北大核心 2008年第6期1475-1477,共3页 Computer Engineering and Design
关键词 典型相关分析 核方法 核典型相关分析 支持向量数据描述(SVDD) 人脸识别 canonical correlation analysis kernel method kernel canonical correlation analysis support vectors data description face recognition
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献10

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共引文献96

同被引文献13

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计算机工程与设计
2008年 第6期

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