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.
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References
Pentland, M., Turk, A.P.: Face Recognition Using Eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition (1991)
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)
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)
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)
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)
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)
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)
Li, S.Z.: Encyclopaedia of Biometrics. Springer (2009)
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)
Al-Assam, H., Sellahewa, H., Jassim, S.A.: Lightweight approach for biometric template protection. In: Proceedings of SPIE (2009)
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)
Castillo, O.Y.G.: Survey about Facial Image Quality. Fraunhofer Institute for Computer Graphics Research, 10–15 (2005)
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)
Youmaran, R., Adler, A.: Measuring biometric sample quality in terms of biometric information. In: Biometric Consortium Conference, 2006 Biometrics Symposium, pp. 1–6 (2006)
Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2002)
Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)
Cover, T., Thomas, J.: Elements of information theory, 2nd edn. (2006)
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)
Jassim, S.A., Sellahewa, H.: A wavelet-based approach to face verification/recognition. In: Proc. SPIE, vol. 5986, p. 77 (2005)
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)
Bovik, A.C., Wang, Z.: A Universal Image Quality Index. IEEE Signal Processing Letters 9(3), 81–84 (2002)
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)
Sellahewa, H., Jassim, S.: Image-Quality-Based Adaptive Face Recognition. IEEE Trans. on Instrumentation and Measurement 59(4), 805–813 (2010)
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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
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DOI: https://doi.org/10.1007/978-3-642-31971-6_1
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