摘要
传统的最大间隔准则在计算类间离散度矩阵时往往忽略了类别之间的差异,但是对于人脸年龄估计,不同年龄标签之间的差异性是非常显著的。因此,在标签之间引入距离度量,提出标签敏感的最大间隔准则维数约减算法。此外,考虑到人脸变老的复杂性,提出两步的局部回归算法——K近邻-标签分布的支持向量回归(K Nearset Neighbors-Label Distribution Support Vector Reressor,KNN-LDSVR),以进行人脸年龄估计。在FGNET数据库子集上提出的人脸年龄估计方法的平均绝对误差为4.1岁,相对于已有的年龄估计方法,性能得到提升。
Traditional maximum margin criterion usually ignores the differences between classes in the computation of the between-class scatter matrix.However,for facial age estimation,the differences between age labels are very significant.Therefore,this paper proposed a novel dimensionality reduction algorithm,called label-sensitive maximum margin criterion（lsMMC）,by introducing a distance metric between the classes.In addition,considering the complicated facial aging process,this paper proposed a two-steps local regression algorithm named K nearest neighbors-label distribution support vector regressor（KNN-LDSVR）for age estimation.The mean absolute error of the proposed facial aging estimation method on the FGNET database subset is 4.1 years,which improves the performance compared with existing age estimation methods.
作者
徐晓玲
金忠
贲圣兰
XU Xiao-ling;JIN Zhong;BEN Sheng-lan(School of Computer Science and Engineering,Nanjing University of Science ＆ Technolog;Key Laboratory of Intelligent Perception and System for High-Dimensional Information of Ministry of Education,Nanjing University of Science ＆ Technolog;School of Electronic Science and Engineering,Nanjing Universit)
出处
《计算机科学》
CSCD
北大核心
2018年第6期284-290,共7页
Computer Science
基金
国家自然科学基金(61373063
61375007
61233011)
国家重点基础研究发展计划(2014CB349303)资助
关键词
最大间隔准则
标签敏感
两步
局部回归
年龄估计
Maximum margin criterion
Label-sensitive
Two-steps
Local regression
Age estimation
