Skip to main content

Edge Multidirectional Binary Pattern Applies to High Resolution Thermal Infrared Face Database

  • Conference paper
  • First Online:
  • 2356 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9428))

Abstract

This paper introduces the establishment of a high resolution thermal infrared face database and presents a new thermal infrared face recognition method based on the Edge Multidirectional Binary Pattern. The high resolution thermal infrared face database is captured by Testo 890-1 High-end infrared digital camera with the image resolution 1280×960 pixels through the Super Resolution Technology. The database collects images from 60 persons, and each person has seven images with variations of poses. A new thermal infrared face recognition method based on Edge Multidirectional Binary Pattern (EMDBP) is also proposed, which fully considers the directional information of the image, and extracts more edge directional information. Experimental results show the new method achieved better performance compared with traditional methods.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ghiass, R.S., Arandjelovic, O., Bendada, H., et al.: Infrared face recognition: a comprehensive review of methodologies and databases. J. Eprint Arxiv 47(9), 2807–2824 (2014)

    Google Scholar 

  2. Hermosilla, G., Javier, R.S., Verschae, R., Correa, M.: A comparative study of thermal face recognition methods in unconstrained environments. J. Pattern Recognition 45, 2445–2459 (2012)

    Article  Google Scholar 

  3. Li, J., Yu, W.X., Kuang, G.Y.: The research on face recognition approaches of infrared imagery. J. Journal of National University of Defense Technology 28(2), 73–76 (2006)

    Google Scholar 

  4. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. J. IEEE Trans. Pattern Anal. Machine Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  5. Yang, W.K., Sun, C.Y., Zhang, L.: A multi-manifold discriminant analysis method for image feature extraction. J. Pattern Recognition 44, 1649–1657 (2011)

    Article  MATH  Google Scholar 

  6. Equinox human identification at a distance database. http://www.equinoxsensors.com/products/HID.html

  7. Cutler, R.: Face Recognition Using Infrared Images and Eigenfaces. Computer Science Technical Report Series (1996)

    Google Scholar 

  8. Wu, S.Q., Zheng, H.G., Kia, A.C., Sim, H.O.: Infrared facial recognition using modified blood perfusion. In: Information and Communications Security 9th International Conference, China (2007)

    Google Scholar 

  9. Li, S.Z., Chu, R., Liao, S., et al.: Illumination invariant face Recognition using near-infrared images. J. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 627–639 (2007)

    Article  Google Scholar 

  10. Buddharhaju, P.: Physiology-based face Recognition in the thermal infrared spectrum. J. IEEE Transactions on PAML 29(4), 613–626 (2007)

    Article  Google Scholar 

  11. Zhang, X., Yang, J., Dong, S., Wang, C., Chen, Y., Wu, C.: Thermal infrared face recognition based on the modified blood perfusion model and improved weber local descriptor. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds.) CCBR 2014. LNCS, vol. 8833, pp. 103–110. Springer, Heidelberg (2014)

    Google Scholar 

  12. Jabid, T., Kabir, M.H., Chae, O.: Gender classification using local directional pattern. In: 2010 International Conference on Pattern Recognition, Turkey (2010)

    Google Scholar 

  13. Lu, Y., Xie, Z.H., Fang, Z,J., Yang, J.C., Wu, S.Q., Li, F.: Time-lapse data oriented infrared face recognition method using block-PCA. In: The International Conference on Multimedia Technology (ICMT), China (2010)

    Google Scholar 

  14. Huang, G.B., Zhou, H., Ding, X., et al.: Extreme Learning Machine for Regression and Multiclass Classification. J. IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society 42(2), 513–529 (2012)

    Article  Google Scholar 

  15. Jun, B.J., Kim, D.J.: Robust face detection using local gradient patterns and evidence accumulation. Pattern Recognition 45(9), 3304–3316 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jucheng Yang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, X., Yang, J., Liu, N., Liu, J. (2015). Edge Multidirectional Binary Pattern Applies to High Resolution Thermal Infrared Face Database. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_60

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25417-3_60

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25416-6

  • Online ISBN: 978-3-319-25417-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics