Abstract
The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card for instance. Recently, boosting algorithms has emerged to boost the performance of simple (weak) classifiers by combining them iteratively. The famous AdaBoost algorithm have been proposed for object detection and applied successfully to face detection. In this paper, we investigate the use of AdaBoost for face authentication to boost weak classifiers based simply on pixel values. The proposed approach is tested on a benchmark database, namely XM2VTS. Results show that boosting only hundreds of classifiers achieved near state-of-the-art results. Furthermore, the proposed approach outperforms similar work on face authentication using boosting algorithms on the same database.
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
Verlinde, P., Chollet, G., Acheroy, M.: Multi-modal identity verification using expert fusion. Information Fusion 1, 17–33 (2000)
Zhang, J., Yan, Y., Lades, M.: Face recognition: Eigenfaces, Elastic Matching, and Neural Nets. In: Proceedings of IEEE, vol. 85, pp. 1422–1435 (1997)
Marcel, S., Bengio, S.: Improving face verification using skin color information. In: Proceedings of the 16th ICPR, IEEE Computer Society Press, Los Alamitos (2002)
Kostin, A., Sadeghi, M., Kittler, J., Messer, K.: On representation spaces for SVM based face verification. In: Proceedings of the COST275 Workshop on The Advent of Biometrics on the Internet, Rome, Italy (2002)
Jonsson, K., Matas, J., Kittler, J., Li, Y.: Learning support vectors for face verification and recognition. In: 4th International Conference on Automatic Face and Gesture Recognition, pp. 208–213 (2000)
Li, Y., Kittler, J., Matas, J.: On matching scores of LDA-based face verification. In: Pridmore, T., Elliman, D. (eds.) Proceedings of the British Machine Vision Conference BMVC2000, British Machine Vision Association (2000)
Turk, M., Pentland, A.: Eigenface for recognition. Journal of Cognitive Neuroscience 3, 70–86 (1991)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)
Belhumeur, P., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 45–58. Springer, Heidelberg (1996)
Pavlovic, V., Garg, A.: Efficient Detection of Objects and Attributes Using Boosting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2001)
Viola, P., Jones, M.: Robust Real-time Object Detection. In: IEEE ICCV Workshop on Statistical and Computational Theories of Vision (2001)
Fröba, B., Stecher, S., Kübleck, C.: Boosting a Haar-Like Feature Set for Face Verification. In: Proceedings of Audio and Video based Person Authentication, pp. 617–624 (2003)
Rowley, H.A., Baluja, S., Kanade, T.: Neural Network-based face detection. Transactions on Pattern Analysis and Machine Intelligence 20 (1998)
Féraud, R., Bernier, O., Viallet, J.E., Collobert, M.: A fast and accurate face detector based on Neural Networks. Transactions on Pattern Analysis and Machine Intelligence 23 (2001)
Farkas, L.: Anthropometry of the Head and Face. Raven Press, Hewlett (1994)
Meir, R., Rätsch, G.: An introduction to Boosting and Leveraging (2003)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the IEEE International Conference on Machine Learning, pp. 148–156 (1996)
Schapire, R., Freund, Y., Bartlett, P., Lee, W.: Boosting the margin: a new explanation to the effectiveness of voting methods. Annals of statistics 26 (1998)
Rätsch, G., Onoda, T., Muller, K.-R.: Soft margins for AdaBoost. Machine Learning 42, 287–320 (2001)
Rätsch, G., Warmuth, M.W.: Efficient Margin Maximization with Boosting. Submitted to JMLR (2002)
Lüttin, J.: Evaluation protocol for the the XM2FDB database (lausanne protocol). Technical Report COM-05, IDIAP (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marcel, S., Rodriguez, Y. (2004). Biometric Face Authentication Using Pixel-Based Weak Classifiers. In: Maltoni, D., Jain, A.K. (eds) Biometric Authentication. BioAW 2004. Lecture Notes in Computer Science, vol 3087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25976-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-25976-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22499-0
Online ISBN: 978-3-540-25976-3
eBook Packages: Springer Book Archive