Advertisement

Understanding Fake Faces

  • Ryota NatsumeEmail author
  • Kazuki Inoue
  • Yoshihiro Fukuhara
  • Shintaro Yamamoto
  • Shigeo Morishima
  • Hirokatsu Kataoka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms. However, although the performance gap appears to be narrowing in terms of accuracy-based expectations, a curious question has arisen; specifically, Face understanding of AI is really close to that of human? In the present study, in an effort to confirm the brain-driven concept, we conduct image-based detection, classification, and generation using an in-house created fake face database. This database has two configurations: (i) false positive face detections produced using both the Viola Jones (VJ) method and convolutional neural networks (CNN), and (ii) simulacra that have fundamental characteristics that resemble faces but are completely artificial. The results show a level of suggestive knowledge that indicates the continuing existence of a gap between the capabilities of recent vision-based face recognition algorithms and human-level performance. On a positive note, however, we have obtained knowledge that will advance the progress of face-understanding models.

Keywords

Face recognition False positives Simulacra 

Notes

Acknowledgments

This study was granted in part by the Strategic Basic Research Program ACCEL of the Japan Science and Technology Agency (JPMJAC1602). Shigeo Morishima was supported by a Grant-in-Aid from Waseda Institute of Advanced Science and Engineering. We have had the support and encouragement of cvpaper.challenege group.

References

  1. 1.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-511–I-518 (2001)Google Scholar
  2. 2.
    Bai, Y., Zhang, Y., Ding, M., Ghanem, B.: Finding tiny faces in the wild with generative adversarial network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  3. 3.
    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1999, pp. 187–194. ACM Press/Addison-Wesley Publishing Co., New York (1999)Google Scholar
  4. 4.
    Tran, L., Liu, X.: Nonlinear 3D face morphable model. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  5. 5.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  6. 6.
    Zhao, J., et al.: Towards pose invariant face recognition in the wild. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  7. 7.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014Google Scholar
  8. 8.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015Google Scholar
  9. 9.
    Baudrillard, J.: Simulacra and simulations: disneyland. In: Lemert, Ch. (ed.) Social Theory. The Multicultural and Classic Readings, pp. 524–530. Westview Press, Boulder (1993)Google Scholar
  10. 10.
    Liu, J., Li, J., Feng, L., Li, L., Tian, J., Lee, K.: Seeing Jesus in toast: neural and behavioral correlates of face pareidolia. Cortex 53, 60–77 (2014)CrossRefGoogle Scholar
  11. 11.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  12. 12.
    Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27, pp. 2672–2680. Curran Associates, Inc. (2014)Google Scholar
  13. 13.
    Zafeiriou, S., Zhang, C., Zhang, Z.: A survey on face detection in the wild: past, present and future. Comput. Vis. Image Underst. 138, 1–24 (2015)CrossRefGoogle Scholar
  14. 14.
    Takahashi, K., Watanabe, K.: Seeing objects as faces enhances object detection. I-Perception 6(5), 2041669515606007 (2015)CrossRefGoogle Scholar
  15. 15.
    Abaci, B., Akgül, T.: Detecting face-looking images. In: 2015 23rd Signal Processing and Communications Applications Conference (SIU), pp. 2186–2189, May 2015Google Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)Google Scholar
  17. 17.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, June 2009Google Scholar
  18. 18.
    Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1452–1464 (2017)CrossRefGoogle Scholar
  19. 19.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015Google Scholar
  20. 20.
  21. 21.
    Face, W.T.: https://www.wtface.com/ (2018)
  22. 22.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)Google Scholar
  23. 23.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016Google Scholar
  24. 24.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10602-1_48CrossRefGoogle Scholar
  25. 25.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. CoRR abs/1511.06434 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ryota Natsume
    • 1
    Email author
  • Kazuki Inoue
    • 1
  • Yoshihiro Fukuhara
    • 1
  • Shintaro Yamamoto
    • 1
  • Shigeo Morishima
    • 1
  • Hirokatsu Kataoka
    • 2
  1. 1.Waseda UniversityShinjukuJapan
  2. 2.National Institute of Advanced Industrial Science and Technology (AIST)TsukubaJapan

Personalised recommendations