Skip to main content
Log in

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

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

In this paper, we present a new hand database called Tecnocampus Hand Image Database that includes right hand, palm and dorsal images. All the images have been acquired with three different sensors (visible, near-infrared and thermal). This database consists of 100 people acquired in five different acquisition sessions, two images per session and palm/dorsal sides. The total amount of pictures is 6.000, and it is mainly developed for hand image biometric recognition purposes. In addition, the database has been studied from the information theory point of view, and we found that this highest level of information is achieved in thermal spectrum. Furthermore, a low level of mutual information between different spectrums is also demonstrated. This opens an interesting research field in multi-sensor data fusion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Espinosa-Duró V, Faundez-Zanuy M, Mekyska J. A new face database simultaneously acquired in visible, near infrared and thermal spectrum. Cogn Comput. 2013 119–135.

  4. Sánchez-Ávila C. Hand databases. Group of Biometrics, Biosignals and Security (GB2S). 2012. http://gb2s.es/. Accessed 18 Nov 2012.

  5. Shobha G, Krishna M, Sharma SC. Development of palm print verification system using biometrics. J Softw. 2006;17:1824–36.

    Article  Google Scholar 

  6. Kumar A, Wong DCM, Shen HC, Jain AK. Personal verification using palmprint and hand geometry biometric. In: Proceedings of the 4th international conference on Audio- and video-based biometric person authentication. 2003 668–678.

  7. Kumar A, Zhang D. Incorporating user quality for performance improvement in hand identification. In: Control, automation, robotics and vision, 2008. ICARCV 2008. 10th International Conference on. 2008. 1133–1136.

  8. Ferrer M, Morales A, Travieso C, Alonso J. Low cost multi-modal biometric identification system based on hand geometry, palm and finger print texture. In: Security technology, 2007 41st Annual IEEE International Carnahan Conference on. 2007. 52–58.

  9. Magalhaes F, Oliveira HP, Matos H, Campilho A. HGC2011—Hand geometric points detection competition database. 2012 http://www.fe.up.pt/hgc2011/. Accessed 18 Nov 2012.

  10. Öden C, Erçil A, Yildiz VT, Kirmizitas H, Büke B. Hand recognition using implicit polynomials and geometric features. In: Proceedings of the Third International Conference on audio- and video-based biometric person authentication, Springer, London, UK. 2001. p. 336–341.

  11. Öden C, Erçil A, Büke B. Combining implicit polynomials and geometric features for hand recognition. Pattern Recogn Lett. 2003;24:2145–52.

    Article  Google Scholar 

  12. Yoruk E, Konukoglu E, Sankur B, Darbon J. Shape-based hand recognition. IEEE T Image Process. 2006;15:1803–15.

    Article  Google Scholar 

  13. Faundez-Zanuy M, Mekyska J, Espinosa-Duró V. On the focusing of thermal images. Pattern Recogn Lett. 2011;32:1548–57.

    Article  Google Scholar 

  14. Sesa-Nogueras E, Faundez-Zanuy M, Mekyska J. An information analysis of in-air and on-surface trajectories in online handwriting. Cogn Comput. 2012;4:195–205.

    Article  Google Scholar 

  15. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27:623–56.

    Article  Google Scholar 

  16. Jae-Chern Yoo, Tae Hee Han. Fast normalized cross-correlation. Circuits, systems and signal processing. 28(6). Springer: 2009. p. 819–843.

  17. Faundez-Zanuy M. Data fusion in biometrics. IEEE Aero El Sys Mag. 2005;20:34–8.

    Article  Google Scholar 

  18. Fabregas J, Faundez-Zanuy M. Biometric dispersion matcher. Pattern Recogn. 2008;41(11):3412–26.

    Article  Google Scholar 

  19. Fabregas J, Faundez-Zanuy M. Biometric dispersion matcher versus LDA. Pattern Recogn. 2009;42(9):1816–23.

    Article  Google Scholar 

Download references

Acknowledgments

This work has been supported by FEDER, MEC, TEC2012-38630-C04-03, GACR 102/12/1104, CZ.1.07/2.3.00/20.0094 and VG20102014033. We also acknowledge Ruben Hernandez-Mingorance for his support during acquisitions. The described research was performed in laboratories supported by the SIX projects; the registration number CZ.1.05/2.1.00/03.0072, the operational program Research and Development for Innovation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marcos Faundez-Zanuy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Faundez-Zanuy, M., Mekyska, J. & Font-Aragonès, X. A New Hand Image Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums. Cogn Comput 6, 230–240 (2014). https://doi.org/10.1007/s12559-013-9230-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-013-9230-3

Keywords

Navigation