CAJ | 학술논문

针对二维边际费希尔分析算法中识别率对近邻大小敏感的问题,以及自然界中人脸图像存在姿态和表情等复杂变化,单一的特征提取方法无法满足进一步提高识别率的问题,本文首先提出基于皮尔逊相关系数的二维无参边际费希尔分析算法,进一步提出二维无参边际费希尔分析和奇异值分解算法模糊融合的人脸识别方法.该融合算法利用奇异值分解和改进的二维边际费希尔方法分别提取人脸图像的代数特征和可鉴别结构化特征,综合利用两类特征的优点,基于模糊决策原理对隶属度融合.在CIS三维实时人脸库、Texas三维人脸库及UMIST人脸库的实验结果表明:该算法的分类性能优于单一的二维边际费希尔分析算法或奇异值分解算法.
침대이유변제비희이분석산법중식별솔대근린대소민감적문제,이급자연계중인검도상존재자태화표정등복잡변화,단일적특정제취방법무법만족진일보제고식별솔적문제,본문수선제출기우피이손상관계수적이유무삼변제비희이분석산법,진일보제출이유무삼변제비희이분석화기이치분해산법모호융합적인검식별방법.해융합산법이용기이치분해화개진적이유변제비희이방법분별제취인검도상적대수특정화가감별결구화특정,종합이용량류특정적우점,기우모호결책원리대대속도융합.재CIS삼유실시인검고、Texas삼유인검고급UMIST인검고적실험결과표명:해산법적분류성능우우단일적이유변제비희이분석산법혹기이치분해산법.
Since the recognition of 2D marginal Fisher analysis is sensitive to the size of neighbors and a sole feature extraction method can't meet the requirements of further improving recognition rate for variations in expression and pose of facial images, 2D Nonparametric Marginal Fisher Analysis (2DNMFA) based on person correlation coefficient is proposed firstly, and then a novel algorithm is proposed which integrated with 2DNMFA and Singular Value Decomposition (SVD). The algorithm extracts algebraic features and identifiable structural characteristics by SVD and 2DMFA method respectively, and then fuses membership degree by using fuzzy decision theory based on the advantages of two characteristics. Experimental results on CIS face database, Texas face database and UMIST face database demonstrate that this algorithm improves the recognition rate and is more robust than 2DMFA or SVD.