Exploiting Relative Entropy and Quality Analysis in Cumulative Partial Biometric Fusion

  • Hisham Al-Assam
  • Ali Abboud
  • Harin Sellahewa
  • Sabah Jassim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7228)


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.


Face Recognition Face Image Local Binary Pattern Relative Entropy Equal Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Pentland, M., Turk, A.P.: Face Recognition Using Eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition (1991)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)zbMATHCrossRefGoogle Scholar
  4. 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. 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)CrossRefGoogle Scholar
  6. 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. 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. 8.
    Li, S.Z.: Encyclopaedia of Biometrics. Springer (2009)Google Scholar
  9. 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. 10.
    Al-Assam, H., Sellahewa, H., Jassim, S.A.: Lightweight approach for biometric template protection. In: Proceedings of SPIE (2009)Google Scholar
  11. 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. 12.
    Castillo, O.Y.G.: Survey about Facial Image Quality. Fraunhofer Institute for Computer Graphics Research, 10–15 (2005)Google Scholar
  13. 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. 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. 15.
    Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2002)CrossRefGoogle Scholar
  16. 16.
    Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Cover, T., Thomas, J.: Elements of information theory, 2nd edn. (2006)Google Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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. 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. 21.
    Bovik, A.C., Wang, Z.: A Universal Image Quality Index. IEEE Signal Processing Letters 9(3), 81–84 (2002)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 23.
    Sellahewa, H., Jassim, S.: Image-Quality-Based Adaptive Face Recognition. IEEE Trans. on Instrumentation and Measurement 59(4), 805–813 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hisham Al-Assam
    • 1
  • Ali Abboud
    • 1
  • Harin Sellahewa
    • 1
  • Sabah Jassim
    • 1
  1. 1.Department of Applied ComputingUniversity of BuckinghamUnited Kingdom

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