Abstract
In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented for face recognition. In 2DHDA, small sample size problem (S3 problem) of Heteroscedastic Discriminant Analysis (HAD) is overcome. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, criterion of 2DHDA, log and rearranging terms are taken, and then the optimal projection matrix is solved by gradient descent algorithm. Thirdly, face images are projected onto the optimal projection matrix, thus the 2DHDA features are extracted. Finally, Nearest Neighbor classifier is selected to perform face recognition. Experimental results show that higher recognition rate is obtained by way of 2DHDA compared with 2DLDA.
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
Yang, J., Zhang, D., Yong, X., Yang, J.Y.: Two-dimensional Discriminant Transform for Face Recognition. Pattern Recognition 38(7), 1125–1129 (2005)
Marios, K., Anastasios, T., Ioannis, P.: Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification. IEEE Transactions on Neural Networks 18, 506–519 (2007)
Peg, H., Jianlin, W., Haesun, P.: Solving the Small Sample Size Problem in Face Recognition using Generalized Discriminant Analysis. Pattern Recognition 39(2), 277–287 (2006)
Ye, J., Janarsan, R., Li, Q.: Two-Dimensional Linear Discriminant Analysis. NIPS, 1569–1576 (2005)
Campbell, N.: Canonical Variate Analysis – a General Formulation. Australian Journal of Statistics 26, 86–96 (1984)
Marco, L., Duin, R.P.W.: Non-iterative Heteroscedastic Linear Dimension Reduction for Two-Class Data from fisher to chernoff. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 508–517. Springer, Heidelberg (2002)
Saon, G., Padmanabhan, M., Gopinath, R., Chen, S.: Maximum Likelihood discriminant Feature Spaces. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 1129–1132 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, JY., Gan, JY., He, SB. (2010). Applications of Two-Dimensional Heteroscedastic Discriminant Analysis in Face Recognition. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-14831-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
eBook Packages: Computer ScienceComputer Science (R0)