DynFace: A Multi-label, Dynamic-Margin-Softmax Face Recognition Model

  • Marius CordeaEmail author
  • Bogdan Ionescu
  • Cristian Gadea
  • Dan Ionescu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)


Convolutional neural networks (CNN), more recently, have greatly increased the performance of face recognition due to its high capability in learning discriminative features. Many of the initial face recognition algorithms reported high performance in the small size Labeled Faces in the Wild (LFW) dataset but fail to deliver same results on larger or different datasets. Ongoing research tries to boost the performance of Face Recognition methods by modifying either the neural network structure or the loss function. This paper proposes two novel additions to the typical softmax CNN used for face recognition: a fusion of facial attributes at feature level and a dynamic margin softmax loss. The new network DynFace was extensively evaluated on extended LFW and much larger MegaFace, comparing its performance against known algorithms. The DynFace achieved state-of-art accuracy at high speed. Results obtained during the carefully designed test experiments, are presented in the end of this paper.


Face recognition Face verification Face identification Convolutional neural networks Deep learning Multi-label Softmax Additive margin softmax 


  1. 1.
    Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: CVPR, pp. 1701–1708 (2014)Google Scholar
  2. 2.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)Google Scholar
  3. 3.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report, pp. 7–49 (2007)Google Scholar
  4. 4.
    Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The MegaFace benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4873–4882 (2016)Google Scholar
  5. 5.
    Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age. arXiv:1710.08092 (2017)
  6. 6.
    Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
  7. 7.
    Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: European Conference on Computer Vision, pp. 87–102. Springer (2016)Google Scholar
  8. 8.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Web-scale training for face identification. In: CVPR, pp. 2746–2754 (2015)Google Scholar
  9. 9.
    Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: SphereFace: deep hypersphere embedding for face recognition. In: CVPR (2017)Google Scholar
  10. 10.
    Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81(2), 155–166 (2009)CrossRefGoogle Scholar
  11. 11.
    Wang, F., Liu, W., Liu, H., Cheng, J.: Additive margin softmax for face verification. arXiv:1801.05599 (2018)CrossRefGoogle Scholar
  12. 12.
    Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)CrossRefGoogle Scholar
  13. 13.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  14. 14.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  15. 15.
    Liu, W., Wen, Y., Yu, Z.: Large-margin softmax loss for convolutional neural networks. In: ICML (2016)Google Scholar
  16. 16.
    Rudd, E.M., Gunther, M., Boult, T.E.: MOON: a mixed objective optimization network for the recognition of facial attributes. In: ECCV (2016)Google Scholar
  17. 17.
    Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)Google Scholar
  18. 18.
    Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: European Conference on Computer Vision, pp. 499–515. Springer (2016)Google Scholar
  19. 19.
    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. Tencent AI Lab (2017)Google Scholar
  20. 20.
    Deng, J., Guo, J., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: arXiv:1801.07698 (2018)
  21. 21.
    Wang, Z., He, K., Fu, Y., Feng, R., Jiang, Y.-G., Xue, X.: Multi-task deep neural network for joint face recognition and facial attribute prediction. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 365–374. ACM (2017)Google Scholar
  22. 22.
    Ranjan, R., Sankaranarayanan, S., Castillo, C.D., Chellappa, R.: An all-in-one convolutional neural network for face analysis. In: Proceedings of the 12th International Conference on Automatic Face & Gesture Recognition (FG), Washington, DC, USA, pp. 17–24 (2017)Google Scholar
  23. 23.
    Ng, H.-W., Winkler, S.: A data-driven approach to cleaning large face datasets. In: IEEE International Conference on Image Processing (ICIP), pp. 343–347 (2014)Google Scholar
  24. 24.
    Fu, Y., Hospedales, T.M., Xiang, T., Gong, S., Yao, Y.: Interestingness prediction by robust learning to rank. In: European Conference on Computer Vision, pp. 488–503. Springer (2014)Google Scholar
  25. 25.
    Klontz, J., Klare, B., Klum, S., Burge, M., Jain, A.: Open source biometric recognition. Biometrics: Theory Appl. Syst. (2013)Google Scholar
  26. 26.
  27. 27.
  28. 28.
  29. 29.
    Biemann, C.: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, pp. 73–80 (2006)Google Scholar
  30. 30.
    King, D.E.: DLib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marius Cordea
    • 1
    Email author
  • Bogdan Ionescu
    • 1
  • Cristian Gadea
    • 2
  • Dan Ionescu
    • 2
  1. 1.Mgestyk TechnologiesOttawaCanada
  2. 2.University of OttawaOttawaCanada

Personalised recommendations