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Implementing a Face Recognition System for Media Companies

  • Arturs SprogisEmail author
  • Karlis Freivalds
  • Elita Cirule
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 838)

Abstract

During the past few years face recognition technologies have greatly benefited from the huge progress in machine learning and now have achieved precision rates that are even comparable with humans. This allows us to apply face recognition technologies more effectively for a number of practical problems in various businesses like media monitoring, security, advertising, entertainment that we previously were not able to do due to low precision rates of existing face recognition technologies. In this paper we discuss how to build a face recognition system for media companies and share our experience gained from implementing one for Latvian national news agency LETA. Our contribution is: which technologies to use, how to build a practical training dataset, how large should it be, how to deal with unknown persons.

Keywords

Face recognition Media companies System implementation 

Notes

Acknowledgement

The research leading to these results has received funding from the research project “Competence Centre of Information and Communication Technologies” of the EU Structural funds, contract No. 1.2.1.1/16/A/007 signed between IT Competence Centre and Central Finance and Contracting Agency, Research No. 2.5 “Automated visual material recognition and annotation system for LETA archive”.

References

  1. 1.
    Cao, X., Wipf, D., Wen, F., Duan, G., Sun, J.: A practical transfer learning algorithm for face verification. In: Proceedings of ICCV (2013)Google Scholar
  2. 2.
    Barkan, O., Weill, J., Wolf, L., Aronowitz, H.: Fast high dimensional vector multiplication face recognition. In: Proceedings of ICCV (2013)Google Scholar
  3. 3.
    Phillips, P.J., et al.: An introduction to the good, the bad, & the ugly face recognition challenge problem. In: FG (2011)Google Scholar
  4. 4.
    Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Computer Vision and Pattern Recognition (2014)Google Scholar
  5. 5.
    LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)CrossRefGoogle Scholar
  6. 6.
    Joliffe, I.T.: Principal Component Analysis. Springer, New York (2002).  https://doi.org/10.1007/b98835CrossRefGoogle Scholar
  7. 7.
    Chen, D., Cao, X., Wang, L., Wen, F., Sun, J.: Bayesian face revisited: a joint formulation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 566–579. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33712-3_41CrossRefGoogle Scholar
  8. 8.
    Xing, E.P., Ng, A.Y., Jordan, M., Russell, S.: Distance metric learning with application to clustering with side-information. In: Proceedings of the 15th Advances in Neural Information Processing Systems (NIPS 2002), pp. 521–528 (2002)Google Scholar
  9. 9.
    Huang, G.B., Learned-Miller, E.: Labeled faces in the wild: updates and new reporting procedures. University of Massachusetts, Amherst, Technical report UM-CS-2014-003 (2014)Google Scholar
  10. 10.
    Ding, C., Tao, D.: Robust face recognition via multimodal deep face representation. IEEE Trans. Multimed. 17(11), 2049–2058 (2015)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Sun, Y., Liang, D., Wang, X., Tang, X.: DeepID3. face recognition with very deep neural networks (2014)Google Scholar
  13. 13.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint: arXiv:1409.1556
  14. 14.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions (2014). arXiv:1409.4842 [cs.CV]
  15. 15.
    Sun, Y., Wang, X., Tang, X.: Hybrid deep learning for face verification. In: Proceedings of ICCV (2013)Google Scholar
  16. 16.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering (2015). arXiv:1503.03832 [cs.CV]
  17. 17.
  18. 18.
    He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_38CrossRefGoogle Scholar
  19. 19.
    Zagoruyko, S., Komodakis, N.: Wide Residual Networks (2016). arXiv:1605.07146 [cs.CV]
  20. 20.
    Srivastava, R.K., Greff, K., Schmidhuber, J.: Deep Learning Workshop (ICML 2015) (2015)Google Scholar
  21. 21.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, vol. 1, pp. 1097–1105 (2012)Google Scholar
  22. 22.
    Liu, J., Deng, Y., Bai, T., Wei, Z., Huang, C.: Targeting ultimate accuracy: Face recognition via deep embedding (2015). arXiv:1506.07310v4 [cs.CV]
  23. 23.
  24. 24.
  25. 25.
    Face recognition using Tensorflow. https://github.com/davidsandberg/facenet
  26. 26.
  27. 27.
  28. 28.
  29. 29.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  30. 30.
    Paikens, P.: Latvian newswire information extraction system and entity knowledge base. In: Human Language Technologies – The Baltic Perspective. Frontiers in Artificial Intelligence and Applications, vol. 268, pp. 119–125. IOS Press (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Arturs Sprogis
    • 1
    Email author
  • Karlis Freivalds
    • 1
  • Elita Cirule
    • 2
  1. 1.Institute of Mathematics and Computer ScienceUniversity of LatviaRigaLatvia
  2. 2.LETARigaLatvia

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