Abstract
Face as a biometric is known to be sensitive to different factors, e.g., illumination condition and pose. The resultant degradation in face image quality affects the system performance. To counteract this problem, we investigate the merit of combining a set of face verification systems incorporating image-related quality measures. We propose a fusion paradigm where the quality measures are quantised into a finite set of discrete quality states, e.g., “good illumination vs. “bad illumination”. For each quality state, we design a fusion classifier. The outputs of these fusion classifiers are then combined by a weighted averaging controlled by the a posteriori probability of a quality state given the observed quality measures. The use of quality states in fusion is compared to the direct use of quality measures where the density of scores and quality are jointly estimated. There are two advantages of using quality states. Firstly, much less training data is needed in the former since the relationship between base classifier output scores and quality measures is not learnt jointly but separately via the conditioning quality states. Secondly, the number of quality states provides an explicit control over the complexity of the resulting fusion classifier. In all our experiments involving XM2VTS well illuminated and dark face data sets, there is a systematic improvement in performance over the baseline method (without using quality information) and the direct use of quality in two types of applications: as a quality-dependent score normalisation procedure and as a quality-dependent fusion method (involving several systems).
Keywords
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fierrez-Aguilar, J., et al.: Kernel-Based Multimodal Biometric Verification Using Quality Signals. In: Defense and Security Symposium, Workshop on Biometric Technology for Human Identification. Proc. of SPIE, vol. 5404, pp. 544–554 (2004)
Bigun, J., et al.: Multimodal Biometric Authentication using Quality Signals in Mobile Communnications. In: 12th Int’l Conf. on Image Analysis and Processing, Mantova, pp. 2–11 (2003)
Toh, K.-A., et al.: Fusion of Auxiliary Information for Multimodal Biometric Authentication. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 678–685. Springer, Heidelberg (2004)
Nandakumar, K., et al.: Quality-based Score Level Fusion in Multibiometric Systems. In: Proc. 18th Int’l Conf. Pattern Recognition (ICPR), Hong Kong, pp. 473–476 (2006)
Kryszczuk, K., et al.: Error Handling in Multimodal Biometric Systems using Reliability Measures. In: Proc. 12th European Conference on Signal Processing, Antalya, Turkey (September 2005)
F.V. Jensen, An Introduction to B ayesian Networks, Springer-Verlag, isbn = 0387915028, year = 1996, address = Secaucus, NJ, USA.
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1999)
Jain, A.K., Dass, S.C., Nandakumar, K.: A Principled Approach to Score Level Fusion in Multimodal Biometric Systems. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 1049–1058. Springer, Heidelberg (2005)
Matas, J., et al.: Comparison of Face Verification Results on the XM2VTS Database. In: Proc. 15th Int’l Conf. Pattern Recognition, vol. 4, Barcelona, pp. 858–863 (2000)
Messer, K., et al.: Performance Characterisation of Face Recognition Algorithms and Their Sensitivity to Severe Illumination Changes. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 1–11. Springer, Heidelberg (2005)
Heusch, G., Rodriguez, Y., Marcel, S.: Local Binary Patterns as an Image Preprocessing for Face Authentication. In: Proc. 7th Int’l Conf. Automatic Face and Gesture Recognition (FGR06), Washington, DC, pp. 9–14. IEEE Computer Society Press, Los Alamitos (2006)
Kittler, J., Li, Y., Matas, J.: On Matching Scores for LDA-based Face Verification. In: British Machine Vision Conference (BMVC) (2000)
Reynolds, D.A., Quatieri, T., Dunn, R.: Speaker Verification Using Adapted Gaussian Mixture Models. Digital Signal Processing 10(1–3), 19–41 (2000)
Cardinaux, F., Sanderson, C., Bengio, S.: User Authentication via Adapted Statistical Models of Face Images. IEEE Trans. on Signal Processing 54(1), 361–373 (2006)
Gross, R., Brajovic, V.: An Image Preprocessing Algorithm for Illumination Invariant Face Recognition. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 10–18. Springer, Heidelberg (2003)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Poh, N., Heusch, G., Kittler, J. (2007). On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-540-72523-7_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72481-0
Online ISBN: 978-3-540-72523-7
eBook Packages: Computer ScienceComputer Science (R0)