Abstract
Linear discriminant analysis (LDA) based methods have been very successful in face recognition. Recently, pre-processing approaches have been used to further improve recognition performance but few investigations have been made into the use of post-processing techniques. This paper intends to explore the feasibility and efficiency of the post-processing technique on LDA’s discriminant vectors. In this paper we propose a Gaussian filtering approach to post-process the discriminant vectors. The results of our experiments demonstrate that, post-processing technique can be used to improve recognition performance.
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
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Computing Surveys 35, 399–458 (2003)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3, 71–86 (1991)
Belhumeour, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Processing 11, 467–476 (2002)
Yilmaz, A., Gokmen, M.: Eigenhill vs. eigenface and eigen edge. Pattern Recognition 34, 181–184 (2001)
Chien, J.T., Wu, C.C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 1644–1649 (2002)
Wu, J., Zhou, Z.-H.: Face recognition with one training image per person. Pattern Recognition Lett. 23, 1711–1719 (2002)
Zhao, W., Chellappa, R., Phillips, P.J.: Subspace Linear Discriminant Analysis for Face Recognition. Tech Report CAR-TR-914, Center for Automation Research, University of Maryland (1999)
Dai, D., Yuen, P.C.: Regularized discriminant analysis and its application to face recognition. Pattern Recognition 36, 845–847 (2003)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1990)
Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognition 34, 2067–2070 (2001)
Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using LDA-based algorithms. IEEE Trans. Neural Networks 14, 195–200 (2003)
The ORL face database. AT&T (Olivetti) Research Laboratories, Cambridge, U.K, Online: Available at http://www.uk.research.att.com/facedatabase.html
Pratt, W.K.: Digital Image Processing, 2nd edn. Wiley, New York (1991)
Liu, W., Wang, Y., Li, S.Z., Tan, T.: Null space approach of Fisher discriminant analysis for face recognition. In: Ito, T., Abadi, M. (eds.) TACS 1997. LNCS, vol. 1281, pp. 32–44. Springer, Heidelberg (1997)
Zheng, W., Zhao, L., Zou, C.: An efficient algorithm to solve the small sample size problem for LDA. Pattern Recognition 37, 1077–1079 (2004)
Zheng, W., Zou, C., Zhao, L.: Real-time face recognition using Gram-Schmidt orthogonalization for LDA. In: The 17th International Conference on Pattern Recognition (ICPR 2004), pp. 403–406 (2004)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)
Liu, C., Wechsler, H.: Enhanced Fisher linear discriminant models for face recognition. In: The 14th International Conference on Pattern Recognition (ICPR 1998), pp. 1368–1372 (1998)
Yang, J., Yang, J.-Y., Frangi, A.F.: Combined Fisherfaces framework. Image and Vision Computing 21, 1037–1044 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, K., Zuo, W., Zhang, D. (2005). Post-processing on LDA’s Discriminant Vectors for Facial Feature Extraction. In: Kanade, T., Jain, A., Ratha, N.K. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2005. Lecture Notes in Computer Science, vol 3546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527923_36
Download citation
DOI: https://doi.org/10.1007/11527923_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27887-0
Online ISBN: 978-3-540-31638-1
eBook Packages: Computer ScienceComputer Science (R0)