Abstract
This paper proposes an age estimation algorithm by refining the label distribution in a deep learning framework. There are two tasks during the training period of our algorithm. The first one finds the optimal parameters of supervised deep CNN by given the label distribution of the training sample as the ground truth, while the second one estimates the variances of label distribution to fit the output of the CNN. These two tasks are performed alternatively and both of them are treated as the supervised learning tasks. The AlexNet and ResNet-50 architectures are adopted as the classifiers and the Gaussian form of the label distribution is assumed. Experiments show that the accuracy of age estimation can be improved by refining label distribution.
W. Shen—This work was supported in part by the National Natural Science Foundation of China under Project 61302125, 61671376 and in part by Natural Science Foundation of Shanghai under Project 17ZR1408500.
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Shen, W., Sun, L., Qiu, S., Li, Q. (2017). Age Estimation by Refining Label Distribution in Deep CNN . In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_10
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