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.
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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)
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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
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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
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