Abstract
Age estimation has always hit people’s eyes. While most previous works have focused on constrained images taken under lab condition, which is far from real-world age estimation. The benchmark we used in this paper is the unconstrained Adience [3], which is believed to better reflect the traits of age in the wild condition. In this paper, we adapted contrastive loss to fine-tune the pre-trained VGG-16 over FG-NET to get a better start point and proposed AvgOut-FC Layer to enhance the performance of the models over Audience. We have achieved better results over the Adience benchmark than previous works, which demonstrated the effectiveness of our methods.
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References
Goodfellow, I.J., Wardefarley, D., Mirza, M., et al.: Maxout networks. Comput. Sci. 1319–1327 (2013)
Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: IEEE Conference on CVPR Workshops (2015)
Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. Trans. Inform. Forensics Secur. 9(12), 1, 2, 5, 6 (2014)
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1. IEEE (2005)
Ozbulak, G., Aytar, Y., Ekenel, H.K.: How transferable are CNN-based features for age and gender classification? In: International Conference of the Biometrics Special Interest Group, pp. 1–6 (2016)
Lanitis, A.: The FG-NET Aging Database (2002). www-prima.inrialpes.fr/FGnet/html/benchmarks.html
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. S. 1097–1105 (2012)
Yosinski, J., et al.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems (2014)
Oquab, M., et al.: Learning and transferring mid-level image representations using convolutional neural networks. In: CVPR. IEEE (2014)
Fu, Y., Guo, G., Huang, T.: Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 32(11), 1955–1976 (2010)
Han, H., Otto, C., Liu, X., Jain, A.: Demographic estimation from face images: human vs. machine performance. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 37(6), 1148–1161 (2015)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: International Conference on Multimedia, pp. S. 675–678. ACM (2014)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRRabs/1409.1556 (2014)
Luo, P., Wang, X., Tang, X.: Hierarchical face parsing via deep learning. In: Proceedings of Conference on Computer Vision Pattern Recognition, vol. 3, pp. 2480–2487. IEEE (2012)
Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deep neural networks. In: Proceedings of Conference on Computer Vision Pattern Recognition, vol. 3, pp. 1653–1660. IEEE (2014)
Acknowledgements
This work was partially supported by Beijing Nova Program under Grant No. Z161100004916088, the National Natural Science Foundation of China (Project 61573068, 61471048, and 61375031), and the Fundamental Research Funds for the Central Universities under Grant No. 2014ZD03-01.
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Lin, J., Zheng, T., Liao, Y., Deng, W. (2017). CNN-Based Age Classification via Transfer Learning. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_14
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DOI: https://doi.org/10.1007/978-3-319-67777-4_14
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