Performance Evaluation of Selected Thermal Imaging-Based Human Face Detectors

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)

Abstract

The paper is devoted to the problem of face detection in thermal imagery. Its aim was to investigate several contemporary general-purpose object detectors known to be accurate when working in visible lighting conditions. Employed classifiers are based on AdaBoost learning method with three types of low-level descriptors, namely Haar–like features, Histogram of Oriented Gradients, and Local Binary Patterns. Additionally, the performance of recently proposed Max-Margin Object-Detection Algorithm joint with HOG feature extractor and Deep Neural Network-based approach have been investigated. Performed experiments, on images taken in controlled and uncontrolled conditions, gathered in our own benchmark database and in a few other databases support final observations and conclusions.

Keywords

Thermovision Biometrics Face detection Haar–like features Histogram of Oriented Gradients Local Binary Patterns AdaBoost Max-Margin Object-Detection Algorithm Deep Neural Network 

References

  1. 1.
    Burduk, R.: The AdaBoost algorithm with the imprecision determine the weights of the observations. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds.) ACIIDS 2014, Part II. LNCS (LNAI), vol. 8398, pp. 110–116. Springer, Cham (2014). doi:10.1007/978-3-319-05458-2_12 CrossRefGoogle Scholar
  2. 2.
    Chang, H., Koschan, A., Abidi, M., Kong, S.G., Won, C.-H.: Multispectral visible and infrared imaging for face recognition. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2008)Google Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)Google Scholar
  4. 4.
    Dowdall, J., Pavlidis, I., Bebis, G.: Face detection in the near-ir spectrum. Image Vis. Comput. 21(7), 565–578 (2001)CrossRefGoogle Scholar
  5. 5.
    Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015)CrossRefGoogle Scholar
  6. 6.
    Forczmański, P., Kukharev, G.: Comparative analysis of simple facial features extractors. J. Real-Time Image Process. 1(4), 239–255 (2007)CrossRefGoogle Scholar
  7. 7.
    Forczmański, P., Kukharev, G., Shchegoleva, N.: Simple and robust facial portraits recognition under variable lighting conditions based on two-dimensional orthogonal transformations. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8156, pp. 602–611. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41181-6_61 CrossRefGoogle Scholar
  8. 8.
    Forczmański, P.: Human face detection in thermal images using an ensemble of cascading classifiers. In: Kobayashi, S., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J. (eds.) Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. AISC, vol. 534, pp. 205–215. Springer, Cham (2017). doi:10.1007/978-3-319-48429-7_19 CrossRefGoogle Scholar
  9. 9.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). doi:10.1007/3-540-59119-2_166 CrossRefGoogle Scholar
  10. 10.
    Ghiass, R.S., Arandjelovic, O., Bendada, H., Maldague, X.: Infrared face recognition: a literature review. In: International Joint Conference on Neural Networks (cs.CV) (2013). arXiv:1306.1603
  11. 11.
    Hermans-Killam, L.: Cool Cosmos/IPAC website, Infrared Processing and Analysis Center. http://coolcosmos.ipac.caltech.edu/image_galleries/ir_portraits.html. Accessed 10 May 2016
  12. 12.
    Jasiński, P., Forczmański, P.: Combined imaging system for taking facial portraits in visible and thermal spectra. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 7. AISC, vol. 389, pp. 63–71. Springer, Cham (2016). doi:10.1007/978-3-319-23814-2_8 CrossRefGoogle Scholar
  13. 13.
    King, D.: Dlib 18.6 released: Make your own object detector! (2015). http://blog.dlib.net/2014/02/dlib-186-released-make-your-own-object.html. Accessed 27 Jan 2017
  14. 14.
    King, D.E.: Max-Margin Object Detection, Computer Vision and Pattern Recognition (cs.CV) (2015). arXiv:1502.00046
  15. 15.
    Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1106–1114 (2012)Google Scholar
  16. 16.
    Miezianko, R.: IEEE OTCBVS WS Series Bench – Terravic Research Infrared Database. http://vcipl-okstate.org/pbvs/bench/. Accessed 20 May 2016
  17. 17.
    Ojala, T., Pietikinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th International Conference on Pattern Recognition, vol. 1, pp. 582–585 (1994)Google Scholar
  18. 18.
    Prokoski, F.J., Riedel, R.: Infrared Identification of Faces and Body Parts. In: BIOMETRICS: Personal Identification in Networked Society. Kluwer (1998)Google Scholar
  19. 19.
    Smiatacz, M.: Liveness measurements using optical flow for biometric person authentication. Metrol. Meas. Syst. 19(2), 257–268 (2012)CrossRefGoogle Scholar
  20. 20.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  21. 21.
    Wong, W.K., Hui, J.H., Lama, J.A.K., Bin Md Desa, J., Izzati, N., Ishak, N.B., Bin Sulaiman, A., Nor, Y.B.M.: Face detection in thermal imaging using head curve geometry. In: 5th International Congress on Image and Signal Processing (CISP 2012), pp. 1038–1041 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

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