AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation
Apparent age estimation from face image has attracted more and more attentions as it is favorable in some realworld applications. In this work, we propose an end-toend 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 webcollected face dataset with identity label, and then it is finetuned 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.
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Datasets
CASIA-WebFace
MORPH
CACD
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Age Estimation | ChaLearn 2015 | AgeNet | e-error | 0.270685 | # 4 |
