SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

作     者：周旭东 陈晓红 钱强 

作者机构：College of Computer Science and TechnologyNanjing University of Aeronautics and Astronautics Information Engineering CollegeYangzhou University College of ScienceNanjing University of Aeronautics and Astronautics 

出 版 物：《Transactions of Nanjing University of Aeronautics and Astronautics》 (南京航空航天大学学报（英文版）)

年 卷 期：2013年第30卷第1期

页      面：77-81页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 13[艺术学] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

基　　金：Supported by the National Natural Science Foundation of China(61170151 61070133 60903130) the Natural Science Research Project of Higher Education of Jiangsu Province(12KJB520018) the Research Foundation of Nanjing University of Aeronautics and Astronautics(NP2011030) 

主　　题：face recognition canonical correlation analysis low-resolution spatial information 

摘      要：Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), *** and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially *** , for a multi-resolution scenario , there are no related *** , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution *** , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.