Skip to main content
Log in

The X-Faces Behind the Portraits of No One

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

This paper presents our work on the computational creation of photorealistic face images with a focus on how we transformed our generative and evolutionary system X-Faces into an interactive Media Art installation entitled Portraits of No One. The X-Faces system resorts to Computer Vision and Computer Graphics to automatically create new face images by recombining facial parts extracted from existing examples, along with Evolutionary Computation and Machine Learning to automatically explore the vast space of composite faces that can be generated. This system consistently generates face images with great photorealism and value, for example, for Data Augmentation or to assess and improve the performance of face detectors. In this paper, we describe how we explored the capabilities of X-Faces in a Media Art context. The result, the interactive installation Portraits of No One, synthesis and displays facial portraits on the borderline between the real and artificial using the facial features captured from its audience. The photorealism of the faces displayed in the installation space invoked the capabilities of Artificial Intelligence to generate content that makes people question its veracity. With the installation Portraits of No One, we allow people to engage with X-Faces and to be part of them.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Photo by José Paulo Ruas/DGPC 2019

Fig. 2
Fig. 3
Fig. 4

Photo by José Paulo Ruas/DGPC 2019

Fig. 5
Fig. 6
Fig. 7

Photo by José Paulo Ruas / DGPC 2019

Fig. 8

Photo by José Paulo Ruas/DGPC 2019

Fig. 9

Photo by José Paulo Ruas/DGPC 2019

Similar content being viewed by others

Notes

  1. Supplementary materials and information about this installation Portraits of No One are available at https://cdv.dei.uc.pt/portraits-of-no-one/.

  2. More details about the hardware of the installation Portraits of No One and its development process is available at https://cdv.dei.uc.pt/portraits-of-no-one/.

References

  1. Correia J, Martins T, Martins P, Machado P. X-Faces: the eXploit is out there. In: Pachet F, Cardoso A, Corruble V, Ghedini F, (eds) Proceedings of the Seventh International Conference on Computational Creativity (ICCC 2016), pp 164–182. Paris, France. 2016.

  2. Correia J, Martins T, Machado P. Evolutionary data augmentation in deep face detection. In: Proceedings of the Genetic and evolutionary computation conference companion (GECCO '19). Association for Computing Machinery, New York, NY, USA, pp. 163–164. 2019. https://doi.org/10.1145/3319619.3322053.

  3. Martins T, Correia J, Rebelo S, Bicker J, Machado P. Portraits of no one: an interactive installation. In: Romero J, Ekárt A, Martins T, Correia J, (eds) Artificial intelligence in music, sound, art and design. Cham: Springer International Publishing, pp. 104–117. 2020. https://doi.org/10.1007/978-3-030-43859-3_8.

  4. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks Commun. ACM. 2017;60(6):84–6. https://doi.org/10.1145/3065386.

    Article  Google Scholar 

  5. Masi I, Trãn AT, Hassner T, Leksut JT, Medioni G. Do we really need to collect millions of faces for effective face recognition? In: Leibe B, Matas J, Sebe N, Welling M, (eds) Computer vision—ECCV 2016. Lecture notes in computer science, Cham: Springer International Publishing, pp. 579–596. 2016. https://doi.org/10.1007/978-3-319-46454-1_35.

  6. Jiang D, Hu Y, Yan S, Zhang L, Zhang H, Gao W. Efficient 3D reconstruction for face recognition. Pattern Recogn. 2005;38(6):787–11. https://doi.org/10.1016/j.patcog.2004.11.004.

    Article  Google Scholar 

  7. Mohammadzade H, Hatzinakos D. Projection into expression subspaces for face recognition from single sample per person. IEEE Trans Affect Comput. 2013;4(1):69–13. https://doi.org/10.1109/T-AFFC.2012.30.

    Article  Google Scholar 

  8. Seyyedsalehi SZ, Seyyedsalehi SA. Simultaneous learning of nonlinear manifolds based on the bottleneck neural network. Neural Process Lett. 2014;40(2):191–18. https://doi.org/10.1007/s11063-013-9322-9.

    Article  Google Scholar 

  9. Nirkin Y, Masi I, Tran Tuan A, Hassner T, Medioni G. On face segmentation, face swapping, and face perception. In: 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018), pp. 98–105. 2018. https://doi.org/10.1109/FG.2018.00024.

  10. Lv J-J, Shao X, Huang J-S, Zhou X-D, Zhou X. Data augmentation for face recognition. Neurocomputing. 2017;230:184–12. https://doi.org/10.1016/j.neucom.2016.12.025.

    Article  Google Scholar 

  11. Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 4396–4405. 2019. https://doi.org/10.1109/CVPR.2019.00453.

  12. Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T. Analyzing and improving the image quality of StyleGAN. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 8107–8116. 2020. https://doi.org/10.1109/CVPR42600.2020.00813.

  13. Karras T, Laine S, Aittala M, Hellsten J, Lehtinen J, Aila T. This person does not exist. 2020. https://thispersondoesnotexist.com/. Accessed 28 Jul 2020.

  14. Chen J, Wang R, Yan S, Shan S, Chen X, Gao W. Enhancing human face detection by resampling examples through manifolds. IEEE Trans Syst Man Cybern Part A Syst Hum. 2007;37(6): 1017–11. https://doi.org/10.1109/TSMCA.2007.906575.

    Article  Google Scholar 

  15. Machado P, Correia J, Romero J. Improving face detection. In: Moraglio A, Silva S, Krawiec K, Machado P, Cotta C (eds) In: 15th european conference on genetic programming (EuroGP), pp. 73–84. 2012. https://doi.org/10.1007/978-3-642-29139-5_7.

  16. Shaw J. In: Video narcissus. 1987. https://www.jeffreyshawcompendium.com/portfolio/video-narcissus/. Accessed 28 Jul 2020.

  17. Kac E. Interfaces 1990. http://www.ekac.org/sstv.html. Accessed 28 Jul 2020.

  18. Biggs S. Solitary. 1992. http://littlepig.org.uk/installations/solitary/solitary.htm. Accessed 28 Jul 2020.

  19. Levin G, Lieberman Z. Reface—Portrait Sequencer. 2007. http://www.flong.com/projects/reface/. Accessed 28 Jul 2020.

  20. Howorka S. Average Face Mirror. 2015. http://www.sarahhoworka.at/projects/average-face-mirror. Accessed 28 Jul 2020.

  21. Al Tawil S. IDEMixer. 2019. http://samehaltawil.com/portfolio/idemixer/. Accessed 28 Jul 2020.

  22. Suhr HC, You I. We: exploring interactive multimedia performance. In: Proceedings of the 27th ACM international conference on multimedia. New York: ACM, pp. 1147–1155. 2019. https://doi.org/10.1145/3343031.3355706.

  23. McDonald K. Sharing Faces. 2013. https://github.com/kylemcdonald/sharingfaces. Accessed 28 Jul 2020.

  24. Lancel K, Maat H, Brazier F. Saving face: playful design for social engagement, in public smart city spaces. In: Brooks AL, Brooks E, Sylla C (eds) Interactivity, game creation, design, learning, and innovation. Cham: Springer International Publishing, pp. 296–305. 2019. https://doi.org/10.1007/978-3-030-06134-0_34.

  25. Lozano-Hemmer R. Level of Confidence. 2015. http://www.lozano-hemmer.com/artworks/level_of_confidence.php. Accessed 28 Jul 2020.

  26. Daniele A. This is not private. 2015. http://www.letitbrain.it/letitbrain/?port=this-is-not-private. Accessed 28 Jul 2020.

  27. Tyka M. Portraits of imaginary people. 2017: http://www.miketyka.com/. Accessed 28 Jul 2020.

  28. Moura JM, Ferreira-Lopes P. Generative face from random data, on “how computers imagine humans.” In: Proceedings of the 8th international conference on digital arts, pp. 85–91. 2017. https://doi.org/10.1145/3106548.3106605.

  29. Klingemann M. Neural Glitch. 2018. http://underdestruction.com/2018/10/28/neural-glitch/. Accessed 28 Jul 2020.

  30. Pérez P, Gangnet M, Blake A. Poisson image editing. ACM Trans Graph (TOG). 2003. https://doi.org/10.1145/882262.882269

    Article  Google Scholar 

Download references

Acknowledgements

The third author is funded by Foundation for Science and Technology (FCT) under the Grant SFRH/BD/132728/2017. This work is supported by national funds through the FCT, Portugal, within the scope of the project UID/CEC/00326/2019 and by European Social Fund, through the Regional Operational Program Centro 2020.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to João Correia, Tiago Martins or Sérgio Rebelo.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Evolution, the New AI Revolution” guest edited by Anikó Ekárt and Anna Isabel Esparcia-Alcázar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Correia, J., Martins, T., Rebelo, S. et al. The X-Faces Behind the Portraits of No One. SN COMPUT. SCI. 2, 236 (2021). https://doi.org/10.1007/s42979-021-00604-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00604-w

Keywords

Navigation