Abstract
The problem of matching a low-resolution (LR) face image to a gallery of high-resolution (HR) face images is addressed in this letter. Previous research has focused on introducing a learning based super-resolution (LBSR) method before matching or transforming LR and HR faces into a unified feature space (UFS) for matching. To identify LR faces, we present a method called coupled discriminant multi-manifold analysis (CDMMA). In CDMMA, we first explore the neighborhood information as well as local geometric structure of the multi-manifold space spanned by the samples. And then, we explicitly learn two mappings to project LR and HR faces to a unified discriminative feature space (UDFS) through a supervised manner, where the discriminative information is maximized for classification. After that, the conventional classification method is applied in the CDMMA for final identification. Experimental results conducted on two standard face recognition databases demonstrate the superiority of the proposed CDMMA.
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References
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)
Zhang, X., Gao, Y.: Face recognition across pose: A review. Pattern Recogn. 42(11), 2876–2896 (2009)
Hennings-Yeomans, P., Baker, S., Kumar, B.: Simultaneous super-resolution and feature extraction for recognition of low-resolution faces. In: CVPR 2008, pp. 1–8 (2008)
Baker, S., Kanade, T.: Hallucinating faces. In: Proc. IEEE Conf. on Automatic Face and Gesture (FG), pp. 83–88 (2000)
Wang, X., Tang, X.: Hallucinating face by eigentransformation. IEEE Trans. Systems, Man, and Cybernetics. Part C 35(3), 425–434 (2005)
Jia, K., Gong, S.: Multi-modal tensor face for simultaneous super-resolution and recognition. In: ICCV, pp. 1683–1690 (2005)
Park, J.-S., Lee, S.-W.: An example-based face hallucination method for single-frame, low-resolution facial images. IEEE Trans. Image Process. 17(10), 1806–1816 (2008)
Yang, J., Tang, H., Ma, Y., Huang, T.: Face hallucination via sparse coding. In: ICIP, pp. 1264–1267 (2008)
Ma, X., Zhang, J., Qi, C.: Hallucinating face by position-patch. Pattern Recogn. 43(6), 2224–2236 (2010)
Jung, C., Jiao, L., Liu, B., Gong, M.: Position-patch based face hallucination using convex optimization. IEEE Signal Proc. Let. 18(6), 367–370 (2011)
Jiang, J., Hu, R., Han, Z., Lu, T., Huang, K.: Position-patch based face hallucination via locality-constrained representation. In: ICME 2012, pp. 212–217 (2012)
Jiang, J., Hu, R., Wang, Z., Han, Z.: Noise robust face hallucination via locality-constrained representation. IEEE Trans. on Multimedia 16(5), 1268–1281 (2014)
Jiang, J., Hu, R., Han, Z., Wang, Z., Lu, T., Chen, J.: Locality-constraint iterative neighbor embedding for face hallucination. In: ICME, pp. 1–6 (2013)
Jiang, J., Hu, R., Wang, Z., Han, Z.: Graph regularized sparse coding for image representation. IEEE Trans. Image Process. 23(10), 4220–4231 (2014)
Chang, H., Yeung, D., Xiong, Y.: Super-resolution through neighbor embedding. In: CVPR, pp. 275–282 (2004)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding 290(5500), 2323–2326 (2000)
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, B., Chang, H., Shan, S., Chen, X.: Low-resolution face recognition via coupled locality preserving mappings. IEEE Signal Proc. Let. 17(1), 20–23 (2010)
Wilman, W.Z., Yuen, P.C.: Very low resolution face recognition problem. IEEE Trans. Image Process. 21(1), 327–340 (2012)
Goldberg, A.B., Zhu, X., Singh, A., Xu, Z., Nowak, R.: Multi-manifold semi-supervised learning. Journal of Machine Learning Research - Proceedings Track, 169–176 (2009)
Zheng, M., Bu, J., Chen, C., Wang, C., Zhang, L., Qiu, G., Cai, D.: Graph regularized sparse coding for image representation. IEEE Trans. Image Process. 20(5), 1327–1336 (2011)
Chung, F.: Spectral graph theory. In: Proc. Regional Conf. Series in Math.
Zhu, L., Zhu, S.: Face recognition based on orthogonal discriminant locality preserving projections. Neurocomputing 70(7-9), 1543–1546 (2007)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)
Chih Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie, Image and Vision Computing 28(5), 807–813 (2010)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
Yang, A.Y., Ganesh, A., Zhou, Z., Sastry, S., Ma, Y.: A review of fast l1-minimization algorithms for robust face recognition, CoRR abs/1007.3753
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Jiang, J., Hu, R., Han, Z., Chen, L., Chen, J. (2015). Coupled Discriminant Multi-Manifold Analysis with Application to Low-Resolution Face Recognition. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_4
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DOI: https://doi.org/10.1007/978-3-319-14445-0_4
Publisher Name: Springer, Cham
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