Skip to main content

Exploiting Relative Entropy and Quality Analysis in Cumulative Partial Biometric Fusion

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((TDHMS,volume 7228))

Abstract

Relative Entropy (RE) of individual’s biometric features is the amount of information that distinguishes the individual from a given population. This paper presents an analysis of RE measures for face biometric in relation to accuracy of face-based authentication, and proposes a RE-based partial face recognition scheme that fuses face regions according to their RE-ranks. We establish that different facial feature extraction techniques (FET) result in different RE values, and compare RE values in PCA features with those for a number of wavelet subband features at different levels of decomposition. We demonstrate that for each of the FETs there is a strong positive correlation between RE and authentication accuracy, and that increased image quality results in increased RE and increased authentication accuracy for all FETs. In fact, severe image quality degradation may result in more than 75% drop in RE values. We also present a regional version of these investigations in order to determine the facial regions that have more influence on accuracy and RE values, and propose a partial face recognition that fuses in a cumulative manner horizontal face regions according to their RE-ranks. We argue that the proposed approach is not only useful when parts of facial images are unavailable but also it outperforms the use of the full face images. Our experiments show that the required percentage of facial images for achieving the optimal performance of face recognition varies from just over 1% to 45% of the face image depending on image quality whereas authentication accuracy improves significantly especially for low quality face images.

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. Pentland, M., Turk, A.P.: Face Recognition Using Eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition (1991)

    Google Scholar 

  2. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)

    Article  Google Scholar 

  3. Penev, P.S., Atick, J.J.: Local feature analysis: A general statistical theory for object repre-sentation. Network: Computation in Neural Systems 7(3), 477–500 (1996)

    Article  MATH  Google Scholar 

  4. Gutta, S., Philomin, V., Trajkovic, M.: An investigation into the use of partial-faces for face recognition. In: International Conference on Automatic Face and Gesture Recognition, pp. 33–38 (2002)

    Google Scholar 

  5. Yi, D., Liao, S., Lei, Z., Sang, J., Li, S.Z.: Partial Face Matching between Near Infrared and Visual Images in MBGC Portal Challenge. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 733–742. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  6. Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2037–2041 (2006)

    Google Scholar 

  7. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Proceedings of the 3rd International Conference on Analysis and Modelling of Faces and Gestures, pp. 168–182 (2007)

    Google Scholar 

  8. Li, S.Z.: Encyclopaedia of Biometrics. Springer (2009)

    Google Scholar 

  9. Adler, A., Youmaran, R., Loyka, S.: Towards a Measure of Biometric Information. In: The Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 210–213 (2006)

    Google Scholar 

  10. Al-Assam, H., Sellahewa, H., Jassim, S.A.: Lightweight approach for biometric template protection. In: Proceedings of SPIE (2009)

    Google Scholar 

  11. Jassim, S., Al-Assam, H., Sellahewa, H.: Improving performance and security of biometrics using efficient and stable random projection techniques. In: Proc. 6th International Symposium on Image and Signal Processing and Analysis, ISPA (2009)

    Google Scholar 

  12. Castillo, O.Y.G.: Survey about Facial Image Quality. Fraunhofer Institute for Computer Graphics Research, 10–15 (2005)

    Google Scholar 

  13. Yen, R.: New Approach for Measuring Facial Image Quality. In: Biometric Quality Workshop II in Proc. National Institute of Standards and Technology, pp. 7–8 (2007)

    Google Scholar 

  14. Youmaran, R., Adler, A.: Measuring biometric sample quality in terms of biometric information. In: Biometric Consortium Conference, 2006 Biometrics Symposium, pp. 1–6 (2006)

    Google Scholar 

  15. Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2002)

    Article  Google Scholar 

  16. Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  17. Cover, T., Thomas, J.: Elements of information theory, 2nd edn. (2006)

    Google Scholar 

  18. Lades, M., Vorbrüggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R.P., Konen, W.: Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. Computers 42(3), 300–311 (1993)

    Article  Google Scholar 

  19. Jassim, S.A., Sellahewa, H.: A wavelet-based approach to face verification/recognition. In: Proc. SPIE, vol. 5986, p. 77 (2005)

    Google Scholar 

  20. Dai, D.-Q., Yuen, P.C.: Wavelet-Based 2-Parameter Regularized Discriminant Analysis for Face Recognition. In: Proc. AVBPA Int’l Conf. Audio and Video-Based Biometric Person Authentication, pp. 137–144 (2003)

    Google Scholar 

  21. Bovik, A.C., Wang, Z.: A Universal Image Quality Index. IEEE Signal Processing Letters 9(3), 81–84 (2002)

    Article  Google Scholar 

  22. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Generative Models for Recognition under Variable Pose and Illumination. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)

    Article  Google Scholar 

  23. Sellahewa, H., Jassim, S.: Image-Quality-Based Adaptive Face Recognition. IEEE Trans. on Instrumentation and Measurement 59(4), 805–813 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Al-Assam, H., Abboud, A., Sellahewa, H., Jassim, S. (2012). Exploiting Relative Entropy and Quality Analysis in Cumulative Partial Biometric Fusion. In: Shi, Y.Q., Katzenbeisser, S. (eds) Transactions on Data Hiding and Multimedia Security VIII. Lecture Notes in Computer Science, vol 7228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31971-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31971-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31970-9

  • Online ISBN: 978-3-642-31971-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics