Skip to main content
SpringerLink
Log in

A Deep Learning Approach to Hair Segmentation and Color Extraction from Facial Images

  • Conference paper
  • First Online:
Book cover Advanced Concepts for Intelligent Vision Systems (ACIVS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11182))

Abstract

In this paper we tackle the problem of hair analysis in unconstrained images. We propose a fully convolutional, multi-task neural network to segment the image pixels into hair, face and background classes. The network also decides if the person is bald or not. The detected hair pixels are analyzed by a color recognition module which uses color features extracted at super-pixel level and a Random Forest Classifier to determine the hair tone (black, blond, brown, red or white grey). To train and test the proposed solution, we manually segment more than 3500 images from a publicly available dataset. The proposed framework was evaluated on three public databases. The experiments we performed together with the hair color recognition rate of 92% demonstrate the efficiency of the proposed solution.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.trylive.com/demos.

  2. 2.

    Please send an email to diana.borza@cs.utcluj.ro to request the annotations.

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Patt. Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  2. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)

    Google Scholar 

  3. Julian, P., Dehais, C., Lauze, F., Charvillat, V., Bartoli, A., Choukroun, A.: Automatic hair detection in the wild. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 4617–4620. IEEE (2010)

    Google Scholar 

  4. Kae, A., Sohn, K., Lee, H., Learned-Miller, E.: Augmenting CRFs with Boltzmann machine shape priors for image labeling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2019–2026 (2013)

    Google Scholar 

  5. King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)

    Google Scholar 

  6. Krupka, A., Prinosil, J., Riha, K., Minar, J., Dutta, M.: Hair segmentation for color estimation in surveillance systems. In: Proceedings of 6th International Conference on Advanced Multimedia, pp. 102–107 (2014)

    Google Scholar 

  7. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV), December 2015

    Google Scholar 

  8. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  9. Muhammad, U.R., Svanera, M., Leonardi, R., Benini, S.: Hair detection, segmentation, and hairstyle classification in the wild. Image Vis. Comput. 71, 25–37 (2018)

    Article  Google Scholar 

  10. Prinosil, J., Krupka, A., Riha, K., Dutta, M.K., Singh, A.: Automatic hair color de-identification. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 732–736. IEEE (2015)

    Google Scholar 

  11. Proença, H., Neves, J.C.: Soft biometrics: Globally coherent solutions for hair segmentation and style recognition based on hierarchical MRFs. IEEE Trans. Inf. Forensics Secur. 12(7), 1637–1645 (2017)

    Article  Google Scholar 

  12. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  13. Rousset, C., Coulon, P.Y.: Frequential and color analysis for hair mask segmentation. In: 2008 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 2276–2279. IEEE (2008)

    Google Scholar 

  14. Sarraf, S.: Hair color classification in face recognition using machine learning algorithms. Am. Sci. Res. J. Eng. Technol. Sci. (ASRJETS) 26(3), 317–334 (2016)

    MathSciNet  Google Scholar 

  15. Shen, Y., Peng, Z., Zhang, Y.: Image based hair segmentation algorithm for the application of automatic facial caricature synthesis. Sci. World J. 2014, 1–10 (2014)

    Google Scholar 

  16. Sinha, P., Balas, B., Ostrovsky, Y., Russell, R.: Face recognition by humans: nineteen results all computer vision researchers should know about. Proc. IEEE 94(11), 1948–1962 (2006)

    Article  Google Scholar 

  17. Sinha, P., Poggio, T.: ‘united’ we stand. Perception 31(1), 133 (2002)

    Article  Google Scholar 

  18. Yacoob, Y., Davis, L.: Detection, analysis and matching of hair. In: 2005 Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 741–748. IEEE (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 28 Memorandumului Street, 400114, Cluj-Napoca, Romania

    Diana Borza

  2. Faculty of Mathematics and Computer Science, Babes-Bolyai University, 1 Mihail Kogalniceanu Street, 400084, Cluj-Napoca, Romania

    Tudor Ileni & Adrian Darabant

Authors
  1. Diana Borza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tudor Ileni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adrian Darabant
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diana Borza .

Editor information

Editors and Affiliations

  1. DGA, Bagneux, France

    Jacques Blanc-Talon

  2. Laboratoire XLIM, Futuroscope Chasseneuil Cedex, France

    David Helbert

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. CSIRO-ICT Centre, Canberra, ACT, Australia

    Dan Popescu

  5. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Borza, D., Ileni, T., Darabant, A. (2018). A Deep Learning Approach to Hair Segmentation and Color Extraction from Facial Images. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01449-0_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01448-3

  • Online ISBN: 978-3-030-01449-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation