AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation

被引:78
作者
Liu, Xin [1 ]
Li, Shaoxin [2 ]
Kan, Meina [1 ]
Zhang, Jie [1 ]
Wu, Shuzhe [1 ]
Liu, Wenxian [1 ]
Han, Hu [1 ]
Shan, Shiguang [1 ]
Chen, Xilin [1 ]
机构
[1] Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
[2] Tencent BestImage Team, Shanghai 100080, Peoples R China
来源
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOP (ICCVW) | 2015年
关键词
IMAGE;
D O I
10.1109/ICCVW.2015.42
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Apparent age estimation from face image has attracted more and more attentions as it is favorable in some real-world applications. In this work, we propose an end-to-end learning approach for robust apparent age estimation, named by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models, i.e., real-value based regression models and Gaussian label distribution based classification models. For both kind of models, large-scale deep convolutional neural network is adopted to learn informative age representations. Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme. Technically, the AgeNet is first pre-trained on a large-scale web-collected face dataset with identity label, and then it is fine-tuned on a large-scale real age dataset with noisy age label. Finally, it is fine-tuned on a small training set with apparent age label. The experimental results on the ChaLearn 2015 Apparent Age Competition demonstrate that our AgeNet achieves the state-of-the-art performance in apparent age estimation.
引用
收藏
页码:258 / 266
页数:9
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