Skip to main content
SpringerLink
Log in
Book cover

Face Recognition Across the Imaging Spectrum pp 139–163Cite as

Understanding Thermal Face Detection: Challenges and Evaluation

  • Chapter
  • First Online:

  • 2132 Accesses

Abstract

In thermal face detection, researchers have generally assumed manual face detection or have designed algorithms that focus on indoor environment. However, facial properties are dependent on body temperature, surrounding environment, and any accessories or occlusion present on the face. For instance, the presence of scarfs, glasses, or any disguise accessories will alter the emitted heat pattern, thereby making it challenging to detect the face in thermal images. Similarly, daytime outdoor image acquisition has certain effects on the heat pattern compared to nighttime (or indoor controlled) image acquisition settings that affect automatic face detection performance. In this research, we provide a thorough understanding of challenges in thermal face detection along with an experimental evaluation of traditional approaches. Further, we adapt the AdaBoost face detector to yield improved performance on face detection in thermal images in both indoor and outdoor environments. We also propose a region of interest selection approach designed specifically for aiding occluded/disguised thermal face detection. Experiments are performed on the Notre Dame thermal face database as well as the IIITD databases that include variations such as disguise, age, and environmental (day/night) factors. The results suggest that while thermal face detection in semi-controlled environments is relatively easy, occlusion and disguise are challenges that require further attention.

Janhavi Agrawal, Aishwarya Pant, Tejas I. Dhamecha: Equal contributions by the student authors.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.iarpa.gov/index.php/research-programs/janus.

References

  1. Bourlai, T., Jafri, Z.: Eye detection in the middle-wave infrared spectrum: towards recognition in the dark. In: IEEE International Workshop on Information Forensics and Security, pp. 1–6 (2011)

    Google Scholar 

  2. Bourlai, T., Ross, A., Chen, C., Hornak, L.: A study on using middle-wave infrared images for face recognition. In: SPIE, Biometric Technology for Human Identification IX, Baltimore, USA (2012)

    Google Scholar 

  3. Bourlai, T.: Mid-wave ir face recognition systems. SPIE Newsroom Magazine-Defense & Security (2013)

    Google Scholar 

  4. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25(11), 120–126 (2000)

    Google Scholar 

  5. Chen, X., Flynn, P.J., Bowyer, K.W.: PCA-based face recognition in infrared imagery: baseline and comparative studies. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 127–134 (2003)

    Google Scholar 

  6. Chen, X., Flynn, P.J., Bowyer, K.W.: IR and visible light face recognition. Comput. Vis. Image Underst. 99(3), 332–358 (2005)

    Article  Google Scholar 

  7. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  8. Dhamecha, T.I., Nigam, A., Singh, R., Vatsa, M.: Disguise detection and face recognition in visible and thermal spectrums. In: IEEE International Conference on Biometrics, pp. 1–8 (2013)

    Google Scholar 

  9. Dhamecha, T.I., Singh, R., Vatsa, M., Kumar, A.: Recognizing disguised faces: human and machine evaluation. PloS one 9(7), e99212 (2014)

    Google Scholar 

  10. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)

    Article  Google Scholar 

  11. Hsu, R.L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)

    Article  Google Scholar 

  12. IEEE OTCBVS WS Series Bench; DOE University Research Program in Robotics under grant DOE-DE-FG02-86NE37968; DOD/TACOM/NAC/ARC Program under grant R01-1344-18; FAA/NSSA grant R01-1344-48/49; Office of Naval Research under grant #N000143010022

    Google Scholar 

  13. Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection, vol. 2. IEEE (2003)

    Google Scholar 

  14. Li, S.Z., Yi, D., Lei, Z., Liao, S.: The CASIA NIR-VIS 2.0 face database. In: IEEE Conference on Computer Vision and Pattern Recognition—Workshops, pp. 348–353 (2013)

    Google Scholar 

  15. Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007)

    Article  Google Scholar 

  16. Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Advances in Biometrics, pp. 828–837. Springer, Berlin (2007)

    Google Scholar 

  17. Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: IEEE International Conference on Image Processing, vol. 1, pp. I-900–I-903 (2002)

    Google Scholar 

  18. Martinez, B., Binefa, X., Pantic, M.: Facial component detection in thermal imagery. In: IEEE Conference on Computer Vision and Pattern Recognition—Workshops (2010)

    Google Scholar 

  19. Osia, N., Bourlai, T.: A spectral independent approach for physiological and geometric based face recognition in the visible, middle-wave and long-wave infrared bands. Image Vis. Comput. 32(11), 847–859 (2014)

    Article  Google Scholar 

  20. Phung, S.L., Bouzerdoum, A., Chai, D.: A novel skin color model in YCbCr color space and its application to human face detection. In: IEEE International Conference on Image Processing, vol. 1, pp. I–289 (2002)

    Google Scholar 

  21. Selinger, A., Socolinsky, D.A.: Face recognition in the dark. In: IEEE Conference on Computer Vision and Pattern Recognition-Workshops (2004)

    Google Scholar 

  22. Singh, S.K., Chauhan, D., Vatsa, M., Singh, R.: A robust skin color based face detection algorithm. Tamkang J. Sci. Eng. 6(4), 227–234 (2003)

    Google Scholar 

  23. Socolinsky, D.A., Selinger, A.: Thermal face recognition in an operational scenario. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-1012–II-1019 (2004)

    Google Scholar 

  24. Socolinsky, D.A., Neuheisel, J.D., Priebe, C.E., De Vinney, J., Marchette, D.: Fast face detection with a boosted CCCD classifier. Technical Report, Johns Hopkins University (2002)

    Google Scholar 

  25. Trujillo, L., Olague, G., Hammoud, R., Hernandez, B.: Automatic feature localization in thermal images for facial expression recognition. In: IEEE Conference on Computer Vision and Pattern Recognition—Workshops, pages 14 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  27. Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Chen, F., Wang, X.: A natural visible and infrared facial expression database for expression recognition and emotion inference. IEEE Trans. Multimedia 12(7), 682–691 (2010)

    Article  Google Scholar 

  28. Wang, S., Liu, Z., Shen, P., Ji, Q.: Eye localization from thermal infrared images. Pattern Recogn. 46(10), 2613–2621 (2013)

    Article  Google Scholar 

  29. Zhang, Z., Yi, D., Lei, Z., Li, S.Z.: Regularized transfer boosting for face detection across spectrum. IEEE Signal Process. Lett. 19(3), 131–134 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

T.I. Dhamecha is partly supported through TCS Ph.D. research fellowship.

Author information

Authors and Affiliations

  1. IIIT Delhi, New Delhi, India

    Janhavi Agrawal, Aishwarya Pant, Tejas I. Dhamecha, Richa Singh & Mayank Vatsa

Authors
  1. Janhavi Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aishwarya Pant
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tejas I. Dhamecha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richa Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mayank Vatsa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mayank Vatsa .

Editor information

Editors and Affiliations

  1. Multispectral Imagery Lab—MILab, LCSEE, West Virginia University, MORGANTOWN, West Virginia, USA

    Thirimachos Bourlai

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Agrawal, J., Pant, A., Dhamecha, T.I., Singh, R., Vatsa, M. (2016). Understanding Thermal Face Detection: Challenges and Evaluation. In: Bourlai, T. (eds) Face Recognition Across the Imaging Spectrum. Springer, Cham. https://doi.org/10.1007/978-3-319-28501-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28501-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28499-6

  • Online ISBN: 978-3-319-28501-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation