Skip to main content
SpringerLink
Log in

Scale-Varying Triplet Ranking with Classification Loss for Facial Age Estimation

  • Conference paper
  • First Online:
Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11365))

Included in the following conference series:

Abstract

In recent years, considerable efforts based on convolutional neural networks have been devoted to age estimation from face images. Among them, classification-based approaches have shown promising results, but there has been little investigation of age differences and ordinal age information. In this paper, we propose a ranking objective with two novel schemes jointly performed with an age classification objective to take ordinal age labels into account. We first introduce relative triplet sampling in which a set of triplets is constructed considering the relative differences in ages. This also addresses the problem of having limited triplet candidates, that occurs in conventional triplet sampling. We then propose the scale-varying ranking constraint, which decides the importance of a relative triplet and adjusts a scale of gradients accordingly. Our adaptive ranking loss with relative sampling not only lowers the generalization error but ultimately has a meaningful performance improvement over the state-of-the-art methods on two well-known benchmarks.

Supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) [2016-0-00562 (R0124-16-0002), Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly], and NRF [NRF-2017M3C4A7066317].

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Ranjan, R., et al.: Unconstrained age estimation with deep convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 109–117 (2015)

    Google Scholar 

  2. Ozbulak, G., Aytar, Y., Ekenel, H.K.: How transferable are CNN-based features for age and gender classification? In: 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–6. IEEE (2016)

    Google Scholar 

  3. Rodríguez, P., Cucurull, G., Gonfaus, J.M., Roca, F.X., Gonzàlez, J.: Age and gender recognition in the wild with deep attention. Pattern Recognit. 72, 563–571 (2017)

    Article  Google Scholar 

  4. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  5. Hong, S., Ryu, J., Im, W., Yang, H.S.: D3: recognizing dynamic scenes with deep dual descriptor based on key frames and key segments. Neurocomputing 273, 611–621 (2018)

    Article  Google Scholar 

  6. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)

    Google Scholar 

  7. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Web-scale training for face identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2746–2754 (2015)

    Google Scholar 

  8. Hong, S., Im, W., Ryu, J., Yang, H.S.: SSPP-DAN: deep domain adaptation network for face recognition with single sample per person. In: International Conference on Image Processing (2017)

    Google Scholar 

  9. Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015)

    Google Scholar 

  10. Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9, 2170–2179 (2014)

    Article  Google Scholar 

  11. 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. 126, 144–157 (2016)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  13. Hou, L., Yu, C.P., Samaras, D.: Squared earth mover’s distance-based loss for training deep neural networks. arXiv preprint arXiv:1611.05916 (2016)

  14. Gao, B.B., Xing, C., Xie, C.W., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Process. 26, 2825–2838 (2017)

    Article  MathSciNet  Google Scholar 

  15. Gao, B.B., Zhou, H.Y., Wu, J., Geng, X.: Age estimation using expectation of label distribution learning. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pp. 712–718. International Joint Conferences on Artificial Intelligence Organization (2018)

    Google Scholar 

  16. Niu, Z., Zhou, M., Wang, L., Gao, X., Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920–4928 (2016)

    Google Scholar 

  17. Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-CNN for age estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  18. Clapés, A., Bilici, O., Temirova, D., Avots, E., Anbarjafari, G., Escalera, S.: From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2373–2382 (2018)

    Google Scholar 

  19. Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1386–1393 (2014)

    Google Scholar 

  20. Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)

    Google Scholar 

  21. Hong, S., Im, W., Yang, H.S.: CBVMR: content-based video-music retrieval using soft intra-modal structure constraint. In: ACM International Conference on Multimedia Retrieval (2018)

    Google Scholar 

  22. Simo-Serra, E., Ishikawa, H.: Fashion style in 128 floats: joint ranking and classification using weak data for feature extraction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 298–307 (2016)

    Google Scholar 

  23. Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML, pp. 507–516 (2016)

    Google Scholar 

  24. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol, 1, p. 1 (2017)

    Google Scholar 

  25. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)

    Google Scholar 

  26. Wang, L., Li, Y., Lazebnik, S.: Learning deep structure-preserving image-text embeddings. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5005–5013 (2016)

    Google Scholar 

  27. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI, pp. 4278–4284 (2017)

    Google Scholar 

  28. Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6

    Chapter  Google Scholar 

  29. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)

    Google Scholar 

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

    Google Scholar 

  31. Van Der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. KAIST, Daejeon, Republic of Korea

    Woobin Im, Sung-Eui Yoon & Hyun S. Yang

  2. SK T-Brain, Seoul, Republic of Korea

    Sungeun Hong

Authors
  1. Woobin Im
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sungeun Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sung-Eui Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hyun S. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung-Eui Yoon .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 434 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Im, W., Hong, S., Yoon, SE., Yang, H.S. (2019). Scale-Varying Triplet Ranking with Classification Loss for Facial Age Estimation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20873-8_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20872-1

  • Online ISBN: 978-3-030-20873-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation