Abstract
There is a growing interest in subspace learning techniques for face recognition such as Gabor face representation. Although the Gabor face representation has received great success in face recognition, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. This paper proposes a novel face recognition method based on discriminant analysis with Gabor tensor representation. We derive a 3rd-order Gabor tensor representation from a complete response set of 40 Gabor filters. Then Generalized Principal Component Analysis (GPCA) is applied to each Gabor feature matrix. After working out 40 times, the feature matrices in a lower dimensional subspace are finally integrated for classification. Experimental results on ORL database and AR database show promising results of the proposed method.
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
Vasilescu, M., Terzopoulos, D.: Multilinear subspace analysis for image ensembles. In: Proc. Computer Vision and Pattern Recognition, Madison, WI, vol. 2, pp. 93–99 (June 2003)
Dimitrova, N., Zhang, H., Shahraray, B., Sezan, I., Huang, T., Za-khor, A.: Applications of video-content analysis and retrieval. Proc. IEEE Multimedia 9(3), 42–55 (2002)
Hamamoto, H., Uchimura, S., Watanabe, M., Yasuda, T., Tomita, S.: Recognition of handwriting numerals using gabor features. In: Proc. IEEE Conf. Pattern Recognit, pp. 250–253 (1996)
Wiskott, L., Fellous, J.M., Kruger, N., Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)
Marcelja, S.: Mathematical Description of the Responses of Simple Cortical Cells. J. Optical Soc. Am. 70(11), 1297–1300 (1980)
Daugman, J.G.: Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile. Vision Research 20, 847–856 (1980)
Daugman, J.G.: Uncertainty Relation for Resolution in Space, Spatial Frequency and Orientation Optimized by Two-Dimen-sional Visual Cortical Filters. J. Optical Soc. Am. 2(7), 1160–1169 (1985)
Liu, C., Wechsler, H.: Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition. IEEE Trans. Image Processing 11(4), 467–476 (2002)
Liu, C.: Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)
Dunn, D., Higgins, W.E., Wakeley, J.: Texture Segmentation Using 2D Gabor Elementary Functions. IEEE Trans. Pattern Analysis and Machine Intelligence 16(2), 130–149 (1994)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neurosci. 3(1), 71–86 (1991)
Yang, J., Zhang, D., Frangi, A., Yang, J.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)
Ye, J., Janardan, R., Li, Q.: GPCA: an efficient dimension reduction scheme for image compression and retrieval. In: KDD 2004, pp. 354–363 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, L., Huang, R., Ye, X., Yang, W., Changyin, S. (2013). GPCA on Gabor Tensor for Face Recognition. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-42057-3_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
eBook Packages: Computer ScienceComputer Science (R0)