[1]曾雪强,罗明珠,陈素芬,等.基于自适应多重多元回归的人脸年龄估计[J].江西师范大学学报(自然科学版),2019,(01):68-75.[doi:10.16357/j.cnki.issn1000-5862.2019.01.12]
　ZENG Xueqiang,LUO Mingzhu,CHEN Sufen,et al.The Facial Age Estimation Based on Adaptive Multivariate Multiple Regression[J].Journal of Jiangxi Normal University:Natural Science Edition,2019,(01):68-75.[doi:10.16357/j.cnki.issn1000-5862.2019.01.12]
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基于自适应多重多元回归的人脸年龄估计()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年01期
页码:
68-75
栏目:
信息科学与技术
出版日期:
2019-02-10

文章信息/Info

Title:
The Facial Age Estimation Based on Adaptive Multivariate Multiple Regression
文章编号:
1000-5862(2019)01-0068-08
作者:
曾雪强1; 2; 罗明珠1; 陈素芬3; 吴水秀2; 万中英2
1.南昌大学信息工程学院,江西 南昌 330031; 2.江西师范大学计算机信息工程学院,江西 南昌 330022; 3.南昌工程学院信息工程学院,江西 南昌 330099
Author(s):
ZENG Xueqiang1; 2; LUO Mingzhu1; CHEN Sufen3; WU Shuixiu2; WAN Zhongying2
1.Information Engineering School,Nanchang University,Nanchang Jiangxi 330031,China; 2.School of Computer and Information Engineering,Jiangxi Normal University,Nanchang Jiangxi 330022,China; 3.School of Information Engineering,Nanchang Institute of Technology,Nanchang Jiangxi 330099,China
关键词:
人脸年龄估计;  自适应多重多元回归;  标记分布学习;  偏最小二乘
Keywords:
facial age estimation;  adaptive multivariate multiple regression;  label distribution learning;  partial least square
分类号:
TP 391
DOI:
10.16357/j.cnki.issn1000-5862.2019.01.12
文献标志码:
A
摘要:
针对基于标记分布学习的多重多元回归模型不能生成和人脸老化趋势一致标记分布的问题,提出自适应多重多元回归的人脸年龄估计方法.在为不同年龄生成具有适合标准差的离散高斯分布的基础上,采用偏最小二乘模型并有效地利用邻近年龄的人脸老化信息进行年龄估计.在MORPH人脸数据库上的对比实验结果表明,该文的人脸年龄估计模型具有更好的性能.
Abstract:
In order to address the problem that traditional multivariate multiple regression based label distribution learning methods cannot generate the label distribution according to the tendency of facial aging,a facial age estimation method based on adaptive multivariate multiple regression has been proposed.The proposed method generates the discrete Gaussian distributions with different standard deviations adapted to different ages,and using partial least square model to effectively utilize adjacent facial ageing information to predict facial age.Our experimental results on the MORPH face database show that the facial age estimation model in the paper has better performance than existing correlation models.

参考文献/References:

[1] Bekhouche S E,Ouafi A,Taleb-Ahmed A,et al.Facial age estimation using BSIF and LBP[EB/OL].[2018-01-06].https://www.researchgate.net/publication/270276061_Facial_age_estimation_using_BSIF_and_LBP.
[2] Lanitis A,Taylor C J,Cootes T F.Toward automatic simulation of aging effects on face images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(4):442-455.
[3] Fu Yun,Huang T S.Human age estimation with regression on discriminative aging manifold[J].IEEE Transactions on Multimedia,2008,10(4):578-584.
[4] Guo Guodong,MuGuowang.Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression[C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR),Colorado Springs,Colorado,USA. IEEE,2011:657-664.
[5] Chao Weilun,Liu Junzuo,Ding Jianjiun.Facial age estimation based on label-sensitive learning and age-oriented regression[J].Pattern Recognition,2013,46(3):628-641.
[6] Rothe R,Timofte R,Van Gool L.Deep expectation of real and apparent age from a single image without facial landmarks[J].International Journal of Computer Vision,2018,126(2/4):144-157.
[7] Liu Hao,Lu Jiwen,Feng Jianjiang,et al.Label-sensitive deep metric learning for facial age estimation[J].IEEE Transactions on Information Forensics and Security,2018,13(2):292-305.
[8] Sahoo T K,Banka H.Multi-feature-based facial age estimation using an incomplete facial aging database[J].Arabian Journal for Science and Engineering,2018:43(12):1-22.
[9] Geng Xin,Yin Chao,Zhou Zhihua.Facial age estimation by learning from label distributions[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(10):2401-2412.
[10] Zeng Xueqiang,Run Xiang,Zou Huaxing.Partial least squares regression based facial age estimation[C]∥IEEE International Conference on Computational Science and Engineering(CSE)and Embedded and Ubiquitous Computing(EUC),Guangzhou,China.IEEE,2017,1:416-421.
[11] Singer M,Krivobokova T,Munk A,et al.Partial least squares for dependent data[J].Biometrika,2016,103(2):351-362.
[12] Stott A E,Kanna S,Mandic D P.Widely linear complex partial least squares for latent subspace regression[J].Signal Processing,2018,152:350-362.
[13] Ramanathan N,Chellappa R.Modeling age progression in young faces[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition,New York,USA IEEE,2006:387-394.
[14] Albert A M,Ricanek Jr K,Patterson E.A review of the literature on the aging adult skull and face:implications for forensic science research and applications[J].Forensic Science International,2007,172(1):1-9.
[15] Panis G,Lanitis A,Tsapatsoulis N,et al.Overview of research on facial ageing using the FG-NET ageing database[J].Iet Biometrics,2016,5(2):37-46.
[16] Geng Xin,Wang Qin,Xia Yu.Facial age estimation by adaptive label distribution learning[C]∥International Conference on Pattern Recognition(ICPR),Stockholm,Sweden IEEE,2014:4465-4470.
[17] 曾雪强,赵丙娟,向润,等.基于偏最小二乘的人脸年龄估计[J].南昌大学学报:工科版,2017,39(4):380-385.
[18] Ricanek K,Tesafaye T.Morph: a longitudinal image database of normal adult age-progression[C]∥International Conference on Automatic Face and Gesture Recognition(FGR),Southampton,UK IEEE,2006:341-345.
[19] Geng Xin,Ji R.Label distribution kearning[J].IEEE Transactions on Knowledge and Data Engineering,2016,28(7):1734-1748.
[20] Hartung J,Knapp G.Multivariate multiple regression[M].Wiley StatsRef:Statistics Reference Online,2014.
[21] Waltz R A,Morales J L,Nocedal J,et al.An interior algorithm for nonlinear optimization that combines line search and trust region steps[J].Mathematical Programming,2006,107(3):391-408.
[22] Guo Guodong,Mu Guowang,Fu Yun,et al.Human age estimation using bio-inspired features[C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR),Miami,Florida,USA IEEE,2009:112-119.
[23] Lan YuanDong,Deng Huifang,Chen Tao.Dimensionality reduction based on neighborhood preserving and marginal discriminant embedding[J].Procedia Engineering,2012,29(4):494-498.

备注/Memo

备注/Memo:
收稿日期:2018-10-09
基金项目:国家自然科学基金(61463033,61866017),江西省教育厅科学技术研究(GJJ150354)和江西省杰出青年人才资助计划(20171BCB23013)资助项目.
作者简介:曾雪强(1978-),男,江西南昌人,教授,博士,博士生导师,主研从事机器学习研究.E-mail:xqzeng@jxnu.edu.cn
更新日期/Last Update: 2019-02-10