Skip to main content
SpringerLink
Log in

Accuracy Enhancement of the Viola-Jones Algorithm for Thermal Face Detection

  • Conference paper
  • First Online:
Intelligent Computing Methodologies (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10363))

Included in the following conference series:

Abstract

Face detection is the first step for many facial analysis applications and has been extensively researched in the visible spectrum. While significant progress has been made in the field of face detection in the visible spectrum, the performance of current face detection methods in the thermal infrared spectrum is far from perfect and unable to cope with real-time applications. As the Viola-Jones algorithm has become a common method of face detection, this paper aims to improve the performance of the Viola-Jones algorithm in the thermal spectrum for detecting faces with or without eyeglasses. A performance comparison has been made of three different features, HOG, LBP, and Haar-like, to find the most suitable one for face detection from thermal images. Additionally, to accelerate the detection speed, a pre-processing stage is added in both training and detecting phases. Two pre-processing methods have been tested and compared, together with the three features. It is found that the proposed process for performance enhancement gave higher detection accuracy (95%) than the Viola-Jones method (90%) and doubled the detection speed as well.

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

References

  1. Reese, K., Zheng, Y., Elmaghraby, A.: A comparison of face detection algorithms in visible and thermal spectrums. In: International Conference on Advances in Computer Science and Application (2012)

    Google Scholar 

  2. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  3. Gupta, B.S.G., Tiwari, A.: Face detection using gabor feature extraction and artificial neural networks. In: Proceedings of ISCET, pp. 18–23 (2010)

    Google Scholar 

  4. Zheng, Y.: Face detection and eyeglasses detection for thermal face recognition. In: Proceedings of SPIE 8300 (2012)

    Google Scholar 

  5. Cheong, Y.K., Yap, V.V., Nisar, H.: A novel face detection algorithm using thermal imaging. In: IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE) (2014)

    Google Scholar 

  6. Mekyska, J., Espinosa-Duro, V., Faundez-Zanuy, M.: Face segmentation: a comparison between visible and thermal images. In: IEEE International Carnahan Conference on Security Technology (ICCST) (2010)

    Google Scholar 

  7. Sumathi, C.P., Santhanam, T., Mahadevi, M.: Automatic facial expression analysis: a survey. Int. J. Comput. Sci. Eng. Surv. 3(6), 47–59 (2012)

    Article  Google Scholar 

  8. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  9. Trujillo, L., et al.: Automatic feature localization in thermal images for facial expression recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  10. Wong, W.K., et al.: Face detection in thermal imaging using head curve geometry. In: The 5th International Congress on Image and Signal Processing (CISP) (2012)

    Google Scholar 

  11. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  13. Wang, S., et al.: A natural visible and infrared facial expression database for expression recognition and emotion inference. IEEE Trans. Multimed. 12(7), 682–691 (2010)

    Article  Google Scholar 

  14. Esposito, A., Capuano, V., Mekyska, J., Faundez-Zanuy, M.: A naturalistic database of thermal emotional facial expressions and effects of induced emotions on memory. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds.) Cognitive Behavioural Systems. LNCS, vol. 7403, pp. 158–173. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34584-5_12

    Chapter  Google Scholar 

  15. Training Image Labeler (2014). http://uk.mathworks.com/help/vision/ref/trainingimagelabeler-app.html

  16. Wang, S., et al.: Eye localization from thermal infrared images. Pattern Recogn. 46(10), 2613–2621 (2013)

    Article  Google Scholar 

  17. Kanwal, N., Bostanci, E., Clark, A.F.: Evaluation method, dataset size or dataset content: how to evaluate algorithms for image matching? J. Math. Imaging Vis. 55(3), 378–400 (2016)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Arwa M. Basbrain, John Q. Gan & Adrian Clark

  2. Faculty of Computing and Information Technology, King Abdul-Aziz University, Jeddah, Kingdom of Saudi Arabia

    Arwa M. Basbrain

Authors
  1. Arwa M. Basbrain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Q. Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adrian Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arwa M. Basbrain .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Basbrain, A.M., Gan, J.Q., Clark, A. (2017). Accuracy Enhancement of the Viola-Jones Algorithm for Thermal Face Detection. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63315-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63314-5

  • Online ISBN: 978-3-319-63315-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation