Advertisement

Facial Surveillance and Recognition in the Passive Infrared Bands

Chapter
Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

This chapter discusses the use of infrared imaging to perform surveillance and recognition where the face is used for recognizing individuals. In particular, it explores properties of the infrared (IR) band, effects of indoor and outdoor illumination on face recognition (FR) and a framework for both homogeneous and heterogeneous FR systems using multi-spectral sensors. The main benefit of mid-wave infrared and long-wave infrared (MWIR, LWIR) camera sensors is the capability to use FR systems when operating in difficult environmental conditions, such as in low light or complete darkness. This allows for the potential to detect and acquire face images of different subjects without actively illuminating the subject, based on their passively emitted thermal signatures. In this chapter, we demonstrate that by utilizing the “passive” infrared band, facial features can be captured irrespective of illumination (e.g. indoor vs. outdoor). For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or IR sensors. Thus, it can be applied in both daytime and nighttime environments. The second framework aims to solve the heterogeneous, cross-spectral FR problem, enabling recognition in the MWIR and LWIR bands based on images of subjects in the visible spectrum.

References

  1. 1.
    Beymer D, Poggio T (2006) Image representation for visual learning. ScienceGoogle Scholar
  2. 2.
    Bourlai T, Kalka N, Ross A, Cukic B, Hornak L (2010) Cross-spectral face verification in short infrared band. In: Proceedings of IEEE, International conference on pattern recognition (ICPR), Istanbul, pp 1343–1347Google Scholar
  3. 3.
    Buddharaju P, Pavlidis P, Tsiamyrtzis P, Bazakos M (2007) Physiology-based face recognition in the thermal infrared spectrum. IEEE Trans Pattern Anal Mach Intell 29(4):613–626CrossRefGoogle Scholar
  4. 4.
    Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: CVPRGoogle Scholar
  5. 5.
    Chen X, Flynn P, Bowyer K (2003) PCA-based face recognition in infrared imagery: baseline and comparative studies. In: Proceedings of IEEE international workshop on analytics and modeling of faces and gestures (AMFG). IEEE, pp 127–134Google Scholar
  6. 6.
    Elguebaly T, Bouguila N (2011) A Bayesian method for infrared face recognition. Mach Vis Beyond Visible Spectr 1:123–138CrossRefGoogle Scholar
  7. 7.
    Fan W, Yeung DY (2004) Image hallucination using neighbor embedding over visual primitive manifolds. In: CVPRGoogle Scholar
  8. 8.
    Mandal T, Majumdar A, Wu Q (2007) Face recognition by curvelet based feature extraction. In: ICIAR, pp 806–817Google Scholar
  9. 9.
    Melzer T, Reiter M, Bischof H (2003) Appearance model based on kernel canonical correlation analysis. Pattern Recogn 36:1961–1971CrossRefGoogle Scholar
  10. 10.
    Mendez H, Martin C, Kittler J, Plasencia Y, Reyes E (2009) Face recognition with lwir imagery using local binary patterns. In: Proceedings of international conference on advances in biometrics (ICB). Springer, Berlin, pp 327–336Google Scholar
  11. 11.
    Nakamura O, Mathur S, Minami T (1991) Identification of human faces based on isodensity maps. IEEE Proc Pattern Recogn 24(3):263–272CrossRefGoogle Scholar
  12. 12.
    NASA (2013) Electromagnetic spectrum. Imagine the universe. http://imagine.gsfc.nasa.gov/science/toolbox/emspectrum1.html. Accessed 19 May 2015
  13. 13.
    Pan Z, Healey G, Prasad M, Tromberg B (2003) Face recognition in hyperspectral images. IEEE Trans Pattern Anal Mach Intell 25(12):1552–1560CrossRefGoogle Scholar
  14. 14.
    Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639CrossRefGoogle Scholar
  15. 15.
    Pietikinen M (2005) Image analysis with local binary patterns. In: Proceedings of Scandinavian conference on image analysis, pp 115–118Google Scholar
  16. 16.
    Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326Google Scholar
  17. 17.
    Socolinsky D, Wolff L, Neuheisel J, Eveland C (2001) Illumination invariant face recognition using thermal infrared imagery. In: Proceedings of IEEE CS conference on computer vision and pattern recognition (CVPR), vol 1, pp 527–534Google Scholar
  18. 18.
    Srivastana A, Liu X. Statistical hypothesis pruning for recognizing faces from infrared images. Image Vis Comput 21(7):651–661Google Scholar
  19. 19.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. Trans Image Proc 19:1635–1650CrossRefGoogle Scholar
  20. 20.
    Trujillo L, Olague G, Hammoud R, Hernandez B (2005) Automatic feature localization in thermal images for facial expression recognition. In: Proceedings of IEEE CS conference on computer vision pattern recognition (CVPR), vol 3, 14Google Scholar
  21. 21.
    Wolff L, Socolinsky D, Eveland C (2001) Quantitative measurement of illumination invariance for face recognition using thermal infrared imagery. In: IEEE workshop on computer vision beyond the visible spectrum: methods and applicationsGoogle Scholar
  22. 22.
    Wu S, Song W, Jiang L, Xie S, Pan F, Yau W, Ranganath S (2005) Infrared face recognition by using blood perfusion data. In: International conference on audio and video-based biometric person authentication, pp 320–328Google Scholar
  23. 23.
    Xie Z, Wu S, Liu G, Fang Z (2009) Infrared face recognition based on blood perfusion and Fisher linear discrimination analysis. In: IST, pp 85–88Google Scholar
  24. 24.
    Zhili W (2002) Fingerprint recognition. University, Honk Kong BaptistGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.West Virginia UniversityMorgantownUSA
  2. 2.University of GeorgiaAthensUSA

Personalised recommendations