Abstract
Based on non-linear Volterra kernels mapping and direct discrimination analysis(DD-Volterra), a novel face recognition algorithm is proposed. Firstly, the original image is segmented into specific sub blocks and seeks functional mapping using truncated Volterra kernels. Next, simultaneous diagonalization obtain Volterra kernel optimal projection matrix. This matrix can discard useless information that exist in the null space of the inter-class. Also, it can reserve discriminative information that exist in the null space of the intra-class. Finally, in the test, each block of the test image is classified separately, voting strategy and nearest neighbor classifier algorithm are used for classification. Experiments show that the proposed DD-Volterra method has better performance for it is more effective than Volterrafaces during the extracting facial feature stage.
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
References
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Belhumeur, P., Hespanha, J., Kriegman, D.: Face recognition: eigenfaces vs. fisherfaces: recognition using class specific projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
He, X., Yan, S., Hu, Y., et al.: Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv. Neural Inf. Process. Syst. 14(6), 585–591 (2002)
Hua, Y., Jie, Y.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recogn. 34(10), 2067–2070 (2001)
Chen, X., Zhang, J.: Optimized discriminant locality preserving projection of gabor feature for biometric recognition. Int. J. Secur. Its Appl. 6(2), 321–328 (2012)
Gu, X., Liu, C., Wang, S., et al.: Feature extraction using adaptive slow feature discriminant analysis. Neurocomputing 154, 139–148 (2015)
Kumar, R., Banerjee, A., Vemuri, B.C.: Volterrafaces: discriminant analysis using volterra kernels. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 150–155 (2009)
Orcioni, S.: Improving the approximation ability of volterra series identified with a cross-correlation method. Nonlinear Dyn. 78(4), 2861–2869 (2014)
Kumar, R., Banerjee, A., Vemuri, B.C., et al.: Trainable convolution filters and their application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1423–1436 (2012)
Li, H., Dong, J., Li, J.P.: Two-dimensional direct discriminant locality preserving projection analysis for face recognition. In: International Symposium on Biometrics and Security Technologies, pp. 18–23 (2014)
Cai, D., He, X., Han, J., et al.: Orthogonal laplacianfaces for face recognition. IEEE Trans. Image Process. 15(11), 3608–3614 (2006)
Shan, H., Cottrell, G.W.: Looking around the backyard helps to recognize faces and digits. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Wang, F., Zhang, C.: Feature extraction by maximizing the average neighborhood margin, pp. 1–8 (2007)
Hua, G., Viola, P.A., Drucker, S.M.: Face recognition using discriminatively trained orthogonal rank one tensor projections. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Cai, D., He, X., Han, J.: Spectral regression for efficient regularized subspace. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
An, S., Liu, W., Venkatesh, S.: Exploiting side information in locality preserving projection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
An, S., Liu, W., Venkatesh, S.: Face recognition using kernel ridge regression. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2007)
Fu, Y., Huang, T.S.: Image classification using correlation tensor analysis. IEEE Trans. Image Process. 17(2), 226–234 (2008)
Pham, D.S., Venkatesh, S.: Robust learning of discriminative projection for multicategory classification on the Stiefel manifold. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2008)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: IEEE International Conference on Automatic Face and Gesture Recognition, Proc. IEEE 25(12), 46–51 (2002)
Acknowledgements
This work is supported by grants by National Natural Science Foundation of China (Grant No. 61303199).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Feng, G., Li, H., Dong, J., Zhang, J. (2016). Direct Discriminant Analysis Using Volterra Kernels for Face Recognition. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_34
Download citation
DOI: https://doi.org/10.1007/978-981-10-3002-4_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3001-7
Online ISBN: 978-981-10-3002-4
eBook Packages: Computer ScienceComputer Science (R0)