Abstract
Canonical correlation analysis (CCA) is a classical but powerful tool for image super-resolution tasks. Since CCA in essence is a linear projection learning method, it usually fails to uncover the nonlinear relationships between high-resolution (HR) and low-resolution (LR) facial image features. In order to solve this issue, we propose a new face hallucination and recognition algorithm based on kernel CCA, where the nonlinear correlation between HR and LR face features can be well depicted by implicit high-dimensional nonlinear mappings determined by specific kernels. First, our proposed method respectively extracts the principal component features from high-resolution and low-resolution facial images for computational efficiency and noise removal. Then, it makes use of kernel CCA to learn the nonlinear consistency of HR and LR facial features. The proposed approach is compared with existing face hallucination algorithms. A number of experimental results on LR face recognition have demonstrated the effectiveness and robustness of our 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
References
Baker, S., Kanade, T.: Hallucinating faces. In: 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 83–88. IEEE, Grenoble (2000)
Chang, H., Yeung, D.Y., Xiong Y.: Super-resolution through neighbor embedding. In: CVPR, pp. 275–282. IEEE, Washington (2004)
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: ICCV, pp. 349–356. IEEE, Kyoto (2009)
Lu, X., Yuan, Y., Yan, P.: Image super-resolution via double sparsity regularized manifold learning. IEEE T-CSVT 23(12), 2022–2033 (2013)
Gao, X., Zhang, K., Tao, D., Li, X.: Image super-resolution with sparse neighbor embedding. IEEE T-IP 21(7), 3194–3205 (2012)
Dong, W., Zhang, L., Lukac, R., Shi, G.: Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE T-IP 22(4), 1382–1394 (2013)
Mallat, S., Yu, G.: Super-resolution with sparse mixing estimators. IEEE T-IP 19(11), 2889–2900 (2010)
He, L., Qi, H., Zaretzki, R.: Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. In: CVPR, pp. 345–352. IEEE, Portland (2013)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, L., Schumaker, L. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012). doi:10.1007/978-3-642-27413-8_47
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comput. Vis. 40(1), 25–47 (2000)
Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE T-PAMI 32(6), 1127–1133 (2010)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). doi:10.1007/978-3-319-10593-2_13
Kim, J., Lee, J.K., Lee K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR, pp. 1646–1654. IEEE, Las Vegas (2016)
Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR, pp. 1874–1883. IEEE, Las Vegas (2016)
Ni, K.S., Nguyen, T.Q.: Image super resolution using support vector regression. IEEE T-IP 16(6), 1596–1610 (2007)
Huang, H., He, H.: Super-resolution method for face recognition using nonlinear mappings on coherent features. IEEE Trans. Neural Netw. 22(1), 121–130 (2011)
Li, Y., Cai, C., Qiu, G., Lam, K.M.: Face hallucination based on sparse local-pixel structure. Pattern Recogn. 47(3), 1261–1270 (2014)
Wang, X., Tang, X.: Hallucinating face by eigentransformation. IEEE Trans. Syst. Man, Cybern. Part C 35(3), 425–434 (2005)
Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant No. 61402203. In addition, it is also supported in part by the National Natural Science Foundation of China under Grant Nos. 61472344, 61611540347, the Natural Science Foundation of Jiangsu Province of China under Grant Nos. BK20161338, BK20170513, and sponsored by Excellent Young Backbone Teacher Project.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhang, Z., Yuan, YH., Li, Y., Li, B., Qiang, JP. (2017). Face Hallucination and Recognition Using Kernel Canonical Correlation Analysis. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-70136-3_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70135-6
Online ISBN: 978-3-319-70136-3
eBook Packages: Computer ScienceComputer Science (R0)