Abstract
In this paper, we present a new approach for face recognition, named Modular Bilinear Discriminant Analysis (MBDA). In a first step, a set of experts is created, each one being trained independently on specific face regions using a new supervised technique named Bilinear Discriminant Analysis (BDA). BDA relies on the maximization of a generalized Fisher criterion based on bilinear projections of face image matrices. In a second step, the experts are combined to assign an identity with a confidence measure to each of the query faces. A series of experiments is performed in order to evaluate and compare the effectiveness of MBDA with respect to BDA and to the Modular Eigenspaces method. The experimental results indicate that MBDA is more effective than both BDA and the Modular Eigenspaces approach for face recognition.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Turk, M.A., Pentland, A.D.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (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, Special Issue on Face Recognition. 19(7), 711–720 (1997)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence. 26(1), 131–137 (2004)
Visani, M., Garcia, C., Laurent, C.: Comparing Robustness of Two-Dimensional PCA and Eigenfaces for Face Recognition. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 717–724. Springer, Heidelberg (2004)
Visani, M., Garcia, C., Jolion, J.M.: Two-Dimensional-Oriented Linear Discriminant Analysis for Face Recognition. In: Proc. of the International Conference on Computer Vision and Graphics (ICCVG 2004) (September 2004) (To appear in Computational Imaging and Vision series)
Swets, D.L., Weng, J.: Using Discriminant Eigenfeatures for Image Retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence. 18(8), 831–836 (1996)
Pentland, A.D., Moghaddam, B., Starner, T.: View-Based and Modular Eigenspaces for Face Recognition. In: Proc. of the IEEE Computer Society Conference on Pattern Recognition, pp. 84–91 (1994)
Price, J.R., Gee, T.F.: Face Recognition Using Direct, Weighted Linear Discriminant Analysis and Modular Subspaces. Pattern Recognition 38(2), 209–219 (2005)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence. 20(3), 226–239 (1998)
Hwang, B.W., Roh, M.C., Lee, S.W.: Performance Evaluation of Face Recognition Algorithms on Asian Face Database. In: Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, May 2004, pp. 278–283 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Visani, M., Garcia, C., Jolion, JM. (2006). Face Recognition Using Modular Bilinear Discriminant Analysis. In: Bres, S., Laurini, R. (eds) Visual Information and Information Systems. VISUAL 2005. Lecture Notes in Computer Science, vol 3736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590064_3
Download citation
DOI: https://doi.org/10.1007/11590064_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30488-3
Online ISBN: 978-3-540-32339-6
eBook Packages: Computer ScienceComputer Science (R0)