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http://dx.doi.org/10.9717/kmms.2018.21.2.173

Face recognition Based on Super-resolution Method Using Sparse Representation and Deep Learning  

Kwon, Ohseol (Shool of Electrical Electronics & Control Eng., Changwon National University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.2, 2018 , pp. 173-180 More about this Journal
Abstract
This paper proposes a method to improve the performance of face recognition via super-resolution method using sparse representation and deep learning from low-resolution facial images. Recently, there have been many researches on ultra-high-resolution images using deep learning techniques, but studies are still under way in real-time face recognition. In this paper, we combine the sparse representation and deep learning to generate super-resolution images to improve the performance of face recognition. We have also improved the processing speed by designing in parallel structure when applying sparse representation. Finally, experimental results show that the proposed method is superior to conventional methods on various images.
Keywords
Face Recognition; Deep Learning; Super-resolution;
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1 C. Dong, C. Loy, K. He, and X. Tang, "Learning a Deep Convolutional Network for Image Super-resolution," Proceeding of European Conference on Computer Vision, pp. 184-199, 2014.
2 B. Zhang, Y. Gao, S. Zhao, and J. Liu, “Local Derivative Pattern Versus Local Binary Pattern: Face Recognition with High-Order Local Pattern Descriptor,” IEEE Transactions on Image Processing, Vol. 19, No. 2, pp. 533-544, 2010.   DOI
3 B. Olshausen and D. Field, “Emergence of Simple-cell Receptive Field Properties by Learning a Sparse Code for Natural Images,” Nature, Vol. 381, No. 13, pp. 607-609, 1996.   DOI
4 K. Gregor and Y. Lecun, "Learning Fast Approximations of Sparse Coding," Proceeding of the 27th International Conference on Machine Learning, pp. 399-406, 2010.
5 J. Sun, Z. Xu, and H. Shum, "Image Super-resolution Using Gradient Profile Prior," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
6 M. Tipping and C. Bishop, Bayesian Image Super-resolution, Advances in Neural Information and Processing Systems 16, 2003.
7 H. Hou and H. Andrews, “Cubic Spline for Image Interpolation and Digital Filtering,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 26, No. 6, pp. 508-517, 1978.   DOI
8 S. Dai, M. Han, W. Xu, Y. Wu, and Y. Gong, "Soft Edge Smoothness Prior for Alpha Channel Super Resolution," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
9 Y. Lee and J. Shim, “Surface Curvature Based 3D Face Image Recognition Using Depth Weighted Hausdorff Distance,” Journal of Korea Multimedia Society, Vol. 8, No. 1, pp. 34-45, 2005.
10 J. Yang, J. Wright, T. Huang, and Y. Ma, “Imaging Super-resolution Via Sparse Representation,” IEEE Transactions on Image Processing, Vol. 21, No. 8, pp. 3467-3478, 2012.   DOI