Skip to main content
Top

2018 | OriginalPaper | Chapter

Face Detection in Painting Using Deep Convolutional Neural Networks

Authors : Olfa Mzoughi, André Bigand, Christophe Renaud

Published in: Advanced Concepts for Intelligent Vision Systems

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The artistic style of paintings constitutes an important information about the painter’s technique. It can provide a rich description of this technique using image processing tools, and particularly using image features. In this paper, we investigate automatic face detection in the Tenebrism style, a particular painting style that is characterized by the use of extreme contrast between the light and dark. We show that convolutional neural network along with an adapted learning base makes it possible to detect faces with a maximum accuracy in this style. This result is particularly interesting since it can be the basis of an illuminant study in the Tenebrism style.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Krizhevsky, A., H., Hinton, G.E.: Imagenet classification with deep CNN. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012) Krizhevsky, A., H., Hinton, G.E.: Imagenet classification with deep CNN. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
2.
go back to reference Lecoutre, A., B., Yger, F.: Recognizing art style automatically in painting with deep learning. In: Proceedings of the 9th Asian Conference on Machine Learning, ACML, vol. 3, pp. 327–342 (2017) Lecoutre, A., B., Yger, F.: Recognizing art style automatically in painting with deep learning. In: Proceedings of the 9th Asian Conference on Machine Learning, ACML, vol. 3, pp. 327–342 (2017)
3.
go back to reference Stork, D.G.: Computer vision, image analysis, and master art: part 1, 2, 3. Artful Media 24, 16–173 (2016) Stork, D.G.: Computer vision, image analysis, and master art: part 1, 2, 3. Artful Media 24, 16–173 (2016)
4.
go back to reference Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015 Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
5.
go back to reference 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)CrossRef 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)CrossRef
6.
go back to reference Farfade, S.S., M.S., Li, L.J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, vol. 2, pp. 643–650 (2015) Farfade, S.S., M.S., Li, L.J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, vol. 2, pp. 643–650 (2015)
7.
go back to reference Farfade, S.S., M., Li, L.J.: Multi-view face detection using deep convolutional neural networks, vol. 24, pp. 118–173. ACM (2015) Farfade, S.S., M., Li, L.J.: Multi-view face detection using deep convolutional neural networks, vol. 24, pp. 118–173. ACM (2015)
8.
go back to reference Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of CVPR 2001, vol. 2, pp. 643–650 (2001) Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of CVPR 2001, vol. 2, pp. 643–650 (2001)
9.
go back to reference Wechsler, H., Toor, A.S.: Modern art challenges face detection. Pattern Recogn. Lett. (2018, in Press) Wechsler, H., Toor, A.S.: Modern art challenges face detection. Pattern Recogn. Lett. (2018, in Press)
10.
go back to reference Zhang, C., Zhang, Z.: A survey of recent advances in face detection (2010) Zhang, C., Zhang, Z.: A survey of recent advances in face detection (2010)
Metadata
Title
Face Detection in Painting Using Deep Convolutional Neural Networks
Authors
Olfa Mzoughi
André Bigand
Christophe Renaud
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-01449-0_28

Premium Partner