Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Diagnosing deep learning models for high accuracy age estimation from a single image | |
Xing, Junhang1; Li, Kai2 ; Hu, Weiming1,2; Yuan, Chunfeng1; Ling, Haibin3
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| Source Publication | PATTERN RECOGNITION
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| 2017-06-01 | |
| Volume | 66Issue:1Pages:106-116 |
| Subtype | Article |
| Abstract | Given a face image, the problem of age estimation is to predict the actual age from the visual appearance of the face. In this work, we investigate this problem by means of the deep learning techniques. We comprehensively diagnose the training and evaluating procedures of the deep learning models for age estimation on two of the largest datasets. Our diagnosis includes three different kinds of formulations for the age estimation problem using five most representative loss functions, as well as three different architectures to incorporate multi-task learning with race and gender classification. We start our diagnoses process from a simple baseline architecture from previous work. With appropriate problem formulation and loss function, we obtain state-of-the-art performance with the simple baseline architecture. By further incorporating our newly proposed deep multitask learning architecture, the age estimation performance is further improved with high-accuracy race and gender classification results obtained simultaneously. With all the insights gained from the diagnosing process, we finally build a deep multi-task age estimation model which obtains a MAE of 2.96 on the Morph II dataset and 5.75 on the WebFace dataset, both of which improve previous best results by a large margin. |
| Keyword | Age Estimation Deep Learning Multi-task Learning |
| WOS Headings | Science & Technology ; Technology |
| DOI | 10.1016/j.patcog.2017.01.005 |
| WOS Keyword | FACE IMAGES |
| Indexed By | SCI |
| Language | 英语 |
| Funding Organization | 973 Basic Research Program of China(2014CB349303) ; Natural Science Foundation of China(61472421, ; CAS(XDB02070003) ; U1636218 ; 61672519 ; 61303178) |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000397371800012 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/15074 |
| Collection | 模式识别国家重点实验室_视频内容安全 |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 3.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Xing, Junhang,Li, Kai,Hu, Weiming,et al. Diagnosing deep learning models for high accuracy age estimation from a single image[J]. PATTERN RECOGNITION,2017,66(1):106-116. |
| APA | Xing, Junhang,Li, Kai,Hu, Weiming,Yuan, Chunfeng,&Ling, Haibin.(2017).Diagnosing deep learning models for high accuracy age estimation from a single image.PATTERN RECOGNITION,66(1),106-116. |
| MLA | Xing, Junhang,et al."Diagnosing deep learning models for high accuracy age estimation from a single image".PATTERN RECOGNITION 66.1(2017):106-116. |
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| File Name/Size | DocType | Version | Access | License | ||
| xing_Diagnosing deep（1247KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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