Abstract
This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropy-based measure, that contain the most useful clusters. The best match to the test face is found when one final face class is retained. The standard UMIST and XM2VTS databases have been utilized to evaluate the performance of the proposed algorithm. Results show that it provides a good solution to the face classification problem.
This is a preview of subscription content, log in via an institution to check access.
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
Lu, J., Plataniotis, K.N.: Boosting face recognition on a large-scale database. In: Proc. IEEE Int. Conf. on Image Processing (ICIP 2002), Rochester, New York, USA, September 22-25 (2002)
Guo, G.D., Zhang, H.J., Li, S.Z.: Pairwise face recognition. In: Proc. 8th IEEE Int. Conf. on Computer Vision, Vancouver, Canada, vol. 2, pp. 282–287 (2001)
Swets, D.L., Weng, J.: Hierarchical discriminant analysis for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(5), 386–401 (1999)
Liu, H.-C., Su, C.-H., Chiang, Y.-H., Hung, Y.-P.: Personalized face verification system using owner-specific cluster-dependent LDA-subspace. In: Proc. of the 17th Int. Conf. on Pattern Recognition (ICPR 2004), August 23-26, vol. 4, pp. 344–347 (2004)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using LDA based algorithms. IEEE Trans. on Neural Networks 14(1), 195–200 (2003)
Kyperountas, M., Tefas, A., Pitas, I.: Methods for improving discriminant analysis for face authentication. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Philadelphia (March 2005)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1990)
Kyperountas, M., Tefas, A., Pitas, I.: Face verification using locally linear discriminant models. In: Proc. IEEE Int. Conf. on Image Processing, San Antonio, September 16-19, vol. 4, pp. 469–472 (2007)
Camastra, F., Verri, A.: A novel kernel method for clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(5), 801–805 (2005)
Koskela, M., Laaksonen, J., Oja, E.: Entropy-based measures for clustering and SOM topology preservation applied to content-based image indexing and retrieval. In: Proc. 17th Int. Conf. on Pattern Recognition, vol. 2, pp. 1005–1009 (2004)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Selecting kernel eigenfaces for face recognition with one training sample per subject. In: Proc. IEEE Int. Conf. on Multimedia and Expo, pp. 1637–1640 (July 2006)
Kyperountas, M., Tefas, A., Pitas, I.: Weighted piecewise LDA for solving the small sample size problem in face verification. IEEE Trans. on Neural Networks 18(2), 506–519 (2007)
Kyperountas, M., Tefas, A., Pitas, I.: Face recognition via adaptive discrimi-nant clustering. In: Proc. IEEE Int. Conf. on Image Processing, San Diego, pp. 2744–2747 (October 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kyperountas, M., Tefas, A., Pitas, I. (2011). Entropy-Based Iterative Face Classification. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N.C., Fairhurst, M.C. (eds) Biometrics and ID Management. BioID 2011. Lecture Notes in Computer Science, vol 6583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19530-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-19530-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19529-7
Online ISBN: 978-3-642-19530-3
eBook Packages: Computer ScienceComputer Science (R0)