Abstract
Recently, sparse representation and collaborative representation based classifiers for face recognition have been proposed and achieved great attention. However, the two linear regression analysis based methods are sensitive to pose variations in the face images. In this paper, we combine the orthogonal Procrustes problem (OPP) with the regression model, and propose a novel method called orthogonal Procrustes problem based regression (OPPR) for face recognition with pose variations. An orthogonal matrix is introduced as an optimal linear transformation to correct the pose of test image to that of training images as far as possible. According to where the matrix is multiplied to deal with pose variations in vertical or horizontal direction, we propose the left or the right side OPP based regression, respectively. What’s more, we further fuse the two models and propose a bilateral OPP based regression. The proposed model is solved via the efficient alternating iterative algorithm and experimental results on public face databases verify the effectiveness of our proposed models for handling pose variations.
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
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. PAMI 31, 210–227 (2009)
Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. PAMI 32(11), 2106–2112 (2010)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation which helps face recognition?. In: ICCV (2011)
Yang, J., Qian, J., Luo, L., Zhang, F., Gao, Y.: Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes (2014). arXiv:1207.0023
Chai, X., Shan, S., Chen, X., Gao, W.: Locally linear regression for pose-invariant face recognition. IEEE Trans. Image Process. 16(7), 1716–1725 (2007)
Sharma, A., Jacobs, D.W.: Bypassing synthesis: Pls for face recognition with pose, low-resolution and sketch. In: CVPR (2011)
Li, A., Shan, S., Chen, X., Gao, W.: Cross-pose face recognition based on partial least squares. Pattern Recogn. Lett. 32(15), 1948–1955 (2011)
Li, A., Shan, S., Gao, W.: Coupled bias-variance tradeoff for cross-pose face recognition. IEEE Trans. Image Process. 21(1), 305–315 (2012)
Gower, J., Dijksterhuis, G.: Procrustes Problems. Oxford University Press, Oxford (2004)
Green, B.F.: The orthogonal approximation of an oblique simple structure in factor analysis. Psychometrika 17, 429–440 (1952)
Hurley, J.R., Cattell, R.B.: The procrustes program: producing direct rotation to test a hypothesized factor structure. Behav. Sci. 6, 258–262 (1962)
Viklands, T.: Algorithms for the weighted orthogonal procrustes problem and other least squares problems (2006)
Bellino, K.: Computational algorithms for face alignment and recognition. M.S thesis, Virginia Polytechnic Institute and State University (2002)
Sujith, K.R., Ramanan, G.V.: Procrustes analysis and moore-penrose inverse based classifiers for face recognition. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds.) IWBRS 2005. LNCS, vol. 3781, pp. 59–66. Springer, Heidelberg (2005)
Yu, X., Huang, J., Zhang, S., Yan, W., Metaxas, D.N.: Pose-free facial landmark fitting via optimized part mixtures and cascaded deformable shape model. In: ICCV (2013)
Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high dimensional feature and its efficient compression for face verification. In: CVPR (2013)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image Vis. Comput. 28(5), 807–813 (2010)
Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression database. IEEE Trans. PAMI 25(12), 1615–1618 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tai, Y., Yang, J., Luo, L., Qian, J. (2015). Orthogonal Procrustes Problem Based Regression with Application to Face Recognition with Pose Variations. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-23989-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23987-3
Online ISBN: 978-3-319-23989-7
eBook Packages: Computer ScienceComputer Science (R0)