CASIA OpenIR  > 模式识别国家重点实验室  > 视频内容安全
D2C: Deep cumulatively and comparatively learning for human age estimation
Li, Kai1; Xing, Junliang1; Hu, Weiming1,2; Maybank, Stephen J.3
Source PublicationPATTERN RECOGNITION
2017-06-01
Volume66Issue:0Pages:95-105
SubtypeArticle
AbstractAge estimation from face images is an important yet difficult task in computer vision. Its main difficulty lies in how to design aging features that remain discriminative in spite of large facial appearance variations. Meanwhile, due to the difficulty of collecting and labeling datasets that contain sufficient samples for all possible ages, the age distributions of most benchmark datasets are often imbalanced, which makes this problem more challenge. In this work, we try to solve these difficulties by means of the mainstream deep learning techniques. Specifically, we use a convolutional neural network which can learn discriminative aging features from raw face images without any handcrafting. To combat the sample imbalance problem, we propose a novel cumulative hidden layer which is supervised by a point-wise cumulative signal. With this cumulative hidden layer, our model is learnt indirectly using faces with neighbouring ages and thus alleviate the sample imbalance problem. In order to learn more effective aging features, we further propose a comparative ranking layer which is supervised by a pair-wise comparative signal. This comparative ranking layer facilitates aging feature learning and improves the performance of the main age estimation task. In addition, since one face can be included in many different training pairs, we can make full use of the limited training data. It is noted that both of these two novel layers are differentiable, so our model is end-to-end trainable. Extensive experiments on the two of the largest benchmark datasets show that our deep age estimation model gains notable advantage on accuracy when compared against existing methods.
KeywordAge Estimation Deep Learning Convolutional Neural Network
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2017.01.007
WOS KeywordFACE IMAGES ; DIMENSIONALITY ; REGRESSION
Indexed BySCI
Language英语
Funding Organization973 basic research program of China(2014CB349303) ; Natural Science Foundation of China(61472421 ; CAS(XDB02070003) ; 61672519 ; U1636218 ; 61303178)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000397371800011
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15080
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China
2.CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100190, PR China
3.Department of Computer Science and Information Systems, Birkbeck College, London WC1E 7HX, United Kingdom
Recommended Citation
GB/T 7714
Li, Kai,Xing, Junliang,Hu, Weiming,et al. D2C: Deep cumulatively and comparatively learning for human age estimation[J]. PATTERN RECOGNITION,2017,66(0):95-105.
APA Li, Kai,Xing, Junliang,Hu, Weiming,&Maybank, Stephen J..(2017).D2C: Deep cumulatively and comparatively learning for human age estimation.PATTERN RECOGNITION,66(0),95-105.
MLA Li, Kai,et al."D2C: Deep cumulatively and comparatively learning for human age estimation".PATTERN RECOGNITION 66.0(2017):95-105.
Files in This Item: Download All
File Name/Size DocType Version Access License
PR17D2C.pdf(1176KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Kai]'s Articles
[Xing, Junliang]'s Articles
[Hu, Weiming]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Kai]'s Articles
[Xing, Junliang]'s Articles
[Hu, Weiming]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Kai]'s Articles
[Xing, Junliang]'s Articles
[Hu, Weiming]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: PR17D2C.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.