Abstract
A novel feature fusion algorithm using multiple orientations and scales for illumination-robust face recognition is proposed in this paper. For a given image, it will firstly be transformed by a group of Gabor filters, and the transformed coefficients be weighted by a vector, which can be determined by a given discriminant criteria and constrained quadratic programming method, then the weighted sum of these vectors is defined as the feature representation of the facial image. The new method provides a framework to regularize and calculate the complex similarity between different scales and orientations features, and it effectively avoids dimensionality curse problem emerged in some concatenation based feature fusion methods. Meanwhile, our method presents a reasonable approach for sensing discriminant orientations and scales according to the optimized weights. The framework can be extended into general multi-modal pattern recognition problems. Experiments using benchmark databases show that our new method obtains competitive performance and improves the recognition result.
This work is partly supported by NSF of China (90920007, 11171354), the Ministry of Education of China (20070558043) and the Postdoctoral Science Foundation of China (2011M501361).
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
Liu, C.C., Dai, D.Q., Yan, H.: Local discriminant wavelet packet coordinates for face recognition. Journal of Machine Learning Research 8, 1165–1195 (2007)
Massot, C., Herault, J.: Model of frequency analysis in the visual cortex and the shape from texture problem. IJCV 76(2), 165–182 (2008)
Lee, T.S.: Image representation using 2D gabor wavelets. IEEE TPAMI 18(10), 959–971 (1996)
Liu, C.J., Wechsler, H.: Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE TIP 11(4), 467–476 (2002)
Shen, L.L., Bai, L.: Gabor wavelets and kernel direct discriminant analysis for face recognition. In: ICPR, pp. 284–287 (2004)
Liu, C.J.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE TPAMI 26(5), 572–581 (2004)
Xie, X., Lam, K.: Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image. IEEE TIP 15(9), 2481–2492 (2006)
Lei, Z., Chu, R., He, R., Liao, S., Li, S.: Face recognition by discriminant analysis with gabor tensor representation. Advances in Biometrics, 87–95 (2007)
Zhang, B., Shan, S., Chen, X., et al.: Histogram of gabor phase patterns: a novel object representation approach for face recognition. IEEE TIP 16(1), 57–68 (2007)
Xie, S., Shan, S.G., Chen, X.L., Chen, J.: Fusing local patterns of gabor magnitude and phase for face recognition. IEEE TIP 19(5), 1349–1361 (2010)
Dai, D.Q., Yan, H.: Wavelets and face recognition. In: Delac, K., Girgic, M. (eds.) Face Recognition, pp. 59–74. I-Tech Education and Publishing (2007)
Lin, Y., Liu, T., Fuh, C.: Multiple kernel learning for dimensionality reduction. IEEE TPAMI 33(6), 1147–1160 (2010)
Yan, F., Mikolajczyk, K., Barnard, M., Cai, H., et al.: ℓ p norm multiple kernel Fisher discriminant analysis for object and image categorisation. In: IEEE CVPR, pp. 3626–3632 (2010)
Wright, J., Yang, A., Ganesh, A., et al.: Robust face recognition via sparse representation. IEEE TPAMI 31(2), 210–227 (2009)
Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE TPAMI 32(11), 2106–2112 (2010)
Zhang, T., Tang, Y.Y., Fang, B., et al.: Face recognition under varying illumination using gradientfaces. IEEE TIP 18(11), 2599–2606 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ren, CX., Dai, DQ., Lai, ZR. (2012). Learning with Multiple Orientations and Scales for Face Recognition. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-33506-8_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
eBook Packages: Computer ScienceComputer Science (R0)