Skip to main content
SpringerLink
Log in

Discriminative Representation Learning for Face Recognition

  • Conference paper
  • First Online:
Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

A significant progress has been made on face recognition in recent years because of the rapid development of deep learning approaches. Deep learning approaches offer a powerful toolbox for tackling many aspects of face recognition, including the search for effective discriminative features. We compare several state-of-the-art face recognition methods and combine different modules from those methods to propose a special approach for discriminative representation learning. Our approach has a special architecture for representation learning combined with a latest design of classification loss function, making it a highly effective solution for uncontrolled face recognition. Experimented on the Labeled Faces in the Wild (LFW), the Celebrities in Frontal-Profile dataset (CFP), and the AgeDB datasets, our approach shows competitive performance to other state-of-the-art methods.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Bulat, A., Tzimiropoulos, G.: How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks). In: ICCV (2017)

    Google Scholar 

  2. Cao, K., Rong, Y., Li, C., Tang, X., Change Loy, C.: Pose-robust face recognition via deep residual equivariant mapping. In: CVPR (2018)

    Google Scholar 

  3. Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age. In: FG (2018)

    Google Scholar 

  4. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)

    Google Scholar 

  5. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR (2019)

    Google Scholar 

  6. Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. arXiv (2013)

    Google Scholar 

  7. Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition (2016)

    Google Scholar 

  8. Han, D., Kim, J., Kim, J.: Deep pyramidal residual networks. In: CVPR (2017)

    Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV (2015)

    Google Scholar 

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  11. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR (2018)

    Google Scholar 

  12. Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments (2008)

    Google Scholar 

  13. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv (2015)

    Google Scholar 

  14. Kang, B.-N., Kim, Y., Kim, D.: Pairwise relational networks for face recognition. In: ECCV (2018)

    Google Scholar 

  15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv (2014)

    Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)

    Google Scholar 

  17. Lin, M., Chen, Q., Yan, S.: Network in network. arXiv (2013)

    Google Scholar 

  18. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: CVPR (2017)

    Google Scholar 

  19. Masi, I., Chang, F.-J., Choi, J., Harel, S., Kim, J., Kim, K., Leksut, J., Rawls, S., Wu, Y., Hassner, T., et al.: Learning pose-aware models for pose-invariant face recognition in the wild. TPAMI 41, 379–393 (2018)

    Article  Google Scholar 

  20. Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: the first manually collected, in-the-wild age database. In: CVPRW (2017)

    Google Scholar 

  21. Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC (2015)

    Google Scholar 

  22. Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., Hinton, G.: Regularizing neural networks by penalizing confident output distributions. arXiv (2017)

    Google Scholar 

  23. Ranjan, R., Castillo, C.D., Chellappa, R.: L2-constrained softmax loss for discriminative face verification. arXiv (2017)

    Google Scholar 

  24. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR (2015)

    Google Scholar 

  25. Sengupta, S., Chen, J.-C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: WACV (2016)

    Google Scholar 

  26. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv (2014)

    Google Scholar 

  27. Song, L., Gong, D., Li, Z., Liu, C., Liu, W.: Occlusion robust face recognition based on mask learning with pairwise differential Siamese network. In: ICCV (2019)

    Google Scholar 

  28. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015)

    Google Scholar 

  29. Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Sig. Process. Lett. 25, 926–930 (2018)

    Article  Google Scholar 

  30. Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W.: CosFace: large margin cosine loss for deep face recognition. In: CVPR (2018)

    Google Scholar 

  31. Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: ECCV (2016)

    Google Scholar 

  32. Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity (2011)

    Google Scholar 

  33. Wu, X.: Learning robust deep face representation. arXiv (2015)

    Google Scholar 

  34. Wu, X., He, R., Sun, Z.: A lightened CNN for deep face representation. arXiv (2015)

    Google Scholar 

  35. Wu, X., He, R., Sun, Z., Tan, T.: A light CNN for deep face representation with noisy labels. IEEE Trans. Inf. Forensics Secur. 13, 2884–2896 (2018)

    Article  Google Scholar 

  36. Yang, J., Ren, P., Zhang, D., Chen, D., Wen, F., Li, H., Hua, G.: Neural aggregation network for video face recognition. In: CVPR (2017)

    Google Scholar 

  37. Yin, B., Tran, L., Li, H., Shen, X., Liu, X.: Towards interpretable face recognition. In: ICCV (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Taiwan University of Science and Technology, Taipei, Taiwan

    Chia-Hao Tang & Gee-Sern Jison Hsu

Authors
  1. Chia-Hao Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gee-Sern Jison Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gee-Sern Jison Hsu .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tang, CH., Hsu, GS.J. (2020). Discriminative Representation Learning for Face Recognition. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_54

Download citation

Publish with us

Policies and ethics

Search

Navigation