Skip to main content
SpringerLink
Log in

Age Estimation by Refining Label Distribution in Deep CNN

  • Conference paper
  • First Online:
Biometric Recognition (CCBR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

Included in the following conference series:

  • 3670 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chang, K.Y., Chen, C.S., Hung, Y.P.: Ordinal hyperplanes ranker with cost sensitivities for age estimation. In: Computer Vision and Pattern Recognition, pp. 585–592 (2011)

    Google Scholar 

  2. Chen, K., Gong, S., Xiang, T., Chen, C.L.: Cumulative attribute space for age and crowd density estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474 (2013)

    Google Scholar 

  3. Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 621–628 (2004). A Publication of the IEEE Systems Man & Cybernetics Society

    Article  Google Scholar 

  4. Geng, X., Yin, C., Zhou, Z.H.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2401–2412 (2013)

    Article  Google Scholar 

  5. Geng, X., Zhou, Z.H., Smithmiles, K.: Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2234–2240 (2007)

    Article  Google Scholar 

  6. Geng, X., Wang, Q., Xia, Y.: Facial age estimation by adaptive label distribution learning. pp. 4465–4470 (2014)

    Google Scholar 

  7. Guo, G., Fu, Y., Dyer, C.R., Huang, T.S.: Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. Image Process. 17(7), 1178–1188 (2008)

    Article  MathSciNet  Google Scholar 

  8. Guo, G., Mu, G.: Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, 20–25 June, pp. 657–664. Colorado Springs, CO, USA (2011)

    Google Scholar 

  9. Guo, G., Mu, G.: Joint estimation of age, gender and ethnicity: CCA vs.PLS. In: IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–6 (2013)

    Google Scholar 

  10. Han, H., Otto, C., Liu, X., Jain, A.K.: Demographic estimation from face images: human vs. machine performance. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1148–1161 (2015)

    Article  Google Scholar 

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint (2015). arXiv:1512.03385

  12. Hou, P., Geng, X., Huo, Z.W., Lv, J.Q.: Semi-supervised adaptive label distribution learning for facial age estimation. In: 31st AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  13. Huo, Z.W., Yang, X., Xing, C., Zhou, Y., Hou, P., Lv, J.Q., Geng, X.: Deep age distribution learning for apparent age estimation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 17–24 (2016)

    Google Scholar 

  14. Guo, G., Mu, G.: Human age estimation: what is the influence across race and gender?. In: Computer Vision and Pattern Recognition Workshops, pp. 71–78 (2010)

    Google Scholar 

  15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  16. Zhou, H., Miller, P., Zhang, J.: Age classification using Radon transform and entropy based scaling SVM. In: BMVC (2011)

    Google Scholar 

  17. Mathias, M., Benenson, R., Pedersoli, M., Gool, L.: Face detection without bells and whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 720–735. Springer, Cham (2014). doi:10.1007/978-3-319-10593-2_47

    Google Scholar 

  18. Panis, G., Lanitis, A., Tsapatsoulis, N., Cootes, T.F.: An overview of research on facial aging using the FG-NET aging database. IET Biometr. 5(2), 37–46 (2015)

    Article  Google Scholar 

  19. Rawls, A.W.: Morph: development and optimization of a longitudinal age progression database. In: Joint Cost 2101 and 2102 International Conference on Biometric ID Management and Multimodal Communication, pp. 17–24 (2009)

    Google Scholar 

  20. Rothe, R., Timofte, R., Gool, L.V.: Dex: deep expectation of apparent age from a single image. In: IEEE International Conference on Computer Vision Workshop, pp. 252–257 (2016)

    Google Scholar 

  21. Rothe, R., Timofte, R., Van Gool, L.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vis. 1–14 (2016). https://doi.org/10.1007/s11263-016-0940-3

  22. Rothe, R., Timofte, R., Gool, L.V.: Some like it hot - visual guidance for preference prediction. In: Conference on Computer Vision and Pattern Recognition, pp. 5553–5561 (2016)

    Google Scholar 

  23. Tan, Z., Zhou, S., Wan, J., Lei, Z., Li, S.Z.: Age estimation based on a single network with soft softmax of aging modeling. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10113, pp. 203–216. Springer, Cham (2017). doi:10.1007/978-3-319-54187-7_14

    Chapter  Google Scholar 

  24. Wang, X., Guo, R., Kambhamettu, C.: Deeply-learned feature for age estimation. In: Applications of Computer Vision, pp. 534–541 (2015)

    Google Scholar 

  25. Yan, S., Wang, H., Tang, X., Huang, T.S.: Learning auto-structured regressor from uncertain nonnegative labels. In: IEEE International Conference on Computer Vision, ICCV 2007, Rio De Janeiro, Brazil, pp. 1–8, October 2007

    Google Scholar 

  26. Yang, M., Zhu, S., Lv, F., Yu, K.: Correspondence driven adaptation for human profile recognition. In: Computer Vision and Pattern Recognition, pp. 505–512 (2011)

    Google Scholar 

  27. Yang, X., Gao, B.B., Xing, C., Huo, Z.W., Wei, X.S., Zhou, Y., et al.: Deep label distribution learning for apparent age estimation. In: IEEE International Conference on Computer Vision Workshop, pp. 344–350 (2015)

    Google Scholar 

  28. Yi, D., Lei, Z., Li, S.Z.: Age estimation by multi-scale convolutional network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 144–158. Springer, Cham (2015). doi:10.1007/978-3-319-16811-1_10

    Google Scholar 

  29. Zhang, Y., Yeung, D.Y.: Multi-task warped Gaussian process for personalized age estimation. In: Computer Vision and Pattern Recognition, pp. 2622–2629 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China

    Wanxia Shen, Li Sun, Song Qiu & Qingli Li

Authors
  1. Wanxia Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Song Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qingli Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Sun .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Computing and Technology, Chinese Academy of Sciences, Beijing, China

    Yong Xu

  5. Shenzhen University, Shenzhen, China

    Linlin Shen

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

  7. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  8. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Qiao

  9. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo

  10. Shenzhen University, Shenzhen, China

    Shiqi Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69923-3_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation