Low-resolution degradation face recognition over long distance based on CCA

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• 作者：Zhenyu Wang ; Wankou Yang ; Xianye Ben
• 关键词：Canonical correlation analysis (CCA) ; Low resolution ; Correlation ; Dimension matching ; Degradation face recognition
• 刊名：Neural Computing & Applications
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：26
• 期：7
• 页码：1645-1652
• 全文大小：1,315 KB
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• 作者单位：Zhenyu Wang (1) (2)
  Wankou Yang (1) (2)
  Xianye Ben (3)
  
  1. School of Automation, Southeast University, Nanjing, 210096, China
  2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, 210096, China
  3. School of Information Science and Engineering, Shandong University, Jinan, 250100, China
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

Canonical correlation analysis (CCA) is a kind of classical multivariate analysis method. Less canonical correlation variables are used to describe the relationship between two variables completely but easily. To get high face recognition rate under low-resolution degradation over a long distance solidly, in this work, CCA is used to extract the correlation between high-resolution face images and low-resolution ones and to find the transform pair between them. Therefore, face images of the same individual with variable resolutions can be matched accurately. This is the first method that uses CCA to do low-resolution degradation face recognition over long distances. We conduct the experiments on the Extended Yale B and ORL database, and the experimental results validate the efficacy of the proposed method. Keywords Canonical correlation analysis (CCA) Low resolution Correlation Dimension matching Degradation face recognition