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A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums

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Abstract

In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three studied spectral bands contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine studied scenarios, we obtained identification rates higher or equal to 98 %, when using a trained combination rule, and two cases of nine when using a fixed rule.

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References

  1. Faundez-Zanuy M. Biometric security technology. IEEE Aerosp Electron Syst Mag. 2006;21(6):15–26.

    Article  Google Scholar 

  2. Zhao W, Chellapa R, editors. Face processing: advanced modeling and methods, 1st ed. Academic Press; 2005. http://store.elsevier.com/Face-Processing-Advanced-Modeling-and-Methods/isbn-9780120884520/.

  3. Espinosa-Duró V, Faundez-Zanuy M, Mekyska J. Beyond cognitive signals. Cogn Comput. 2011;3:374–81. Springer.

    Article  Google Scholar 

  4. Espinosa-Duró V, Faundez-Zanuy M, Mekyskya J, Monte E. A criterion for analysis of different sensor combinations with an application to face biometrics. Cogn Comput. 2010;2(3):135–41.

    Article  Google Scholar 

  5. Light L, Kayra-Stuart F, Hollander S. Recognition memory for typical and unusual faces. J Exp Psychol Hum Learn Mem. 1979;5:212–28.

    Article  CAS  Google Scholar 

  6. Brennan SE. The caricature generator. Leonardo. 1985;18:170–8.

    Article  Google Scholar 

  7. Hizem W, Allano L, Mellakh A, Dorizzi B. Face recognition from synchronised visible, near-infrared images. IET Signal Process. 2009;3(4):282–8.

    Article  Google Scholar 

  8. Socolinsky DA, Selinger A. Thermal face recognition in an operational scenario. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition (CVPR’04). Vol. 2. 2004. pp. II-1012–II-1019.

  9. Viola P, Jones M. Robust real-time object detection. Technical Report CRL 2001/01, Cambridge Research Laboratory, 2001.

  10. Mekyska J, Espinosa-Duró V, Faundez-Zanuy M. Face segmentation: a comparison between visible and thermal images. In: IEEE 44th international Carnahan conference on security technology ICCST 2010, San José, USA. 5–8 Oct 2010.

  11. Faundez-Zanuy M, Roure-Alcobé J, Espinosa-Duró V, Ortega JA. An efficient face verification method in a transformed domain. Pattern Recogn Lett. 2007;28/7:854–8. Elsevier.

    Article  Google Scholar 

  12. Turk M, Pentland A. Eigenfaces for recognition. Journal Cognitive Neuroscience. 1991;3(1):71–86. Massachusetts Institute of Technology.

    Article  Google Scholar 

  13. Jain AK. Fundamentals of digital image processing. New York: Prentice Hall; 1989.

    Google Scholar 

  14. Vivaracho C, Faundez-Zanuy M, Gaspar JM. An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances. Pattern Recogn. 2009;42(1):183–93. Elsevier.

    Article  Google Scholar 

  15. Mekyska J, Faundez-Zanuy M, Smekal Z, Fabregas J. Score fusion in text-dependent speaker recognition systems. Lect Notes Comput Sci. 2011;6800:120–32.

    Article  Google Scholar 

  16. Franois D, Wert V. The concentration of fractional distances. IEEE Trans Knowl Data Eng. 2007;19(7):873–86.

    Article  Google Scholar 

  17. Faundez-Zanuy M. Data fusion in biometrics. IEEE Aerosp Electron Syst Mag. 2005;20(1):34–8.

    Article  Google Scholar 

  18. Kwon OK, Kong SG. Multiscale fusion of visual and thermal images for robust face recognition. In: IEEE international conference on computational intelligence for homeland security and personal safety. Apr 2005. pp. 112–116

  19. Moon S, Kong SG, Yoo JH, Chung K. Face recognition with multiscale data fusion of visible and thermal images. In: IEEE international conference on computational intelligence for homeland security and personal safety. pp. 24–27. Oct 2006.

  20. Bhowmik MK, Bhattacharjee D, Nasipuri M, Basu DK, Kundu M. Classification of fused images using radial basis function neural network for human face recognition. In: IEEE 2009 world congress on nature & biologically inspired computing (NaBIC 2009). 2009. pp. 19–24.

  21. Bhowmik MK, Bhattacharjee D, Nasipuri M, Basu DK, Kundu M. Optimum fusion of visual and thermal face images for recognition. In: 2010 IEEE sixth international conference on information assurance and security. 2010. pp. 311–316.

  22. Singh R, Vatsa M, Noore A. Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition. Pattern Recogn. 2008;41:880–93.

    Article  Google Scholar 

  23. Neagoe VE, Ropot AD, Mugioiu AC. Real time face recognition using decision fusion of neural classifiers in the visible and thermal infrared spectrum. In: IEEE conference on advanced video and signal based surveillance. 2007. pp. 301–306.

  24. Pop FM, Gordan M, Florea C, Vlaicu A. Fusion based approach for thermal and visible face recognition under pose and expressivity variation. In: 9th RoEduNet IEEE international conference. 2010. pp. 61–66.

  25. Buyssens P, Revenu M. Fusion levels of visible and infrared modalities for face recognition. In: 2010 fourth IEEE international conference on biometrics: theory applications and systems (BTAS). 2010. pp. 1–6.

  26. Raghavendra R, Dorizzi B, Rao A, Kumar GH. Particle swarm optimization based fusion of near infrared and visible images for improved face verification. Pattern Recogn. 2011;44:401–11.

    Article  Google Scholar 

  27. Arandjelovic O, Hammoud R, Cipolla R. Thermal and reflectance based personal identification methodology under variable illumination. Pattern Recogn. 2010;43:1801–13.

    Article  Google Scholar 

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Acknowledgments

This work has been supported by FEDER, MEC, TEC2009-14123-C04-04, KONTAKT-ME 10123, SIX (CZ.1.05/2.1.00/03.0072), CZ.1.07/2.3.00/20.0094 and VG20102014033.

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Authors and Affiliations

  1. EUP Mataró, Tecnocampus, Avda. Ernest Lluch 32, 08302, Mataro, Barcelona, Spain

    Virginia Espinosa-Duró & Marcos Faundez-Zanuy

  2. SPLab, Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic

    Jiří Mekyska

Authors
  1. Virginia Espinosa-Duró
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  2. Marcos Faundez-Zanuy
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  3. Jiří Mekyska
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Corresponding author

Correspondence to Marcos Faundez-Zanuy.

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Espinosa-Duró, V., Faundez-Zanuy, M. & Mekyska, J. A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums. Cogn Comput 5, 119–135 (2013). https://doi.org/10.1007/s12559-012-9163-2

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  • DOI: https://doi.org/10.1007/s12559-012-9163-2

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