Nadia AL-Hassan
ABSTRACT
The characteristics of surveillance video generally include low-resolution images and blurred images. Decreases in image resolution lead to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. Dictionary based super resolution pre-processing techniques have been developed to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process, being ill-posed, and results in visual artifacts that can be visually distracting to humans and/or affect machine feature extraction and face recognition algorithms. In this paper, we investigate the impact of two existing super-resolution methods to reconstruct a high resolution from single/ multiple low-resolution images on face recognition. We propose an alternative scheme that is based on dictionaries in high frequency wavelet subbands. The performance of the proposed method will be evaluated on databases of high and low-resolution images captured under different illumination conditions and at different distances. We shall demonstrate that the proposed approach at level 3 DWT decomposition has superior performance in comparison to the other super resolution methods.
- Zhao, W., et al. 2003. Face recognition: A literature survey. Acm Computing Surveys (CSUR), ACM, Issue 4, Vol. 35, pp. 399--458. Google Scholar
Digital Library
- Tsai, P.S. and Acharya, T. 2006. Image up-sampling using discrete wavelet transform. Joint Conf. Information Services, Advances in Intelligent Systems Research, (Oct.2006).Google Scholar
- Luong, H.Q., Ledda, A. and Philips, W. 2006. Non-local image interpolation. Image Processing, IEEE International conference on, pp. 693--696 (Oct.2006).Google Scholar
- Al-Obaydy, W. and Sellahewa, H. 2011. On using high-definition body worn cameras for face recognition from a distance. Biometrics and ID Management, Springer-Verlag, pp. 193--204. Google ScholarDigital Library
- Yang, J., et al. 2008. Image super-resolution as sparse representation of raw image patches. In Proc.IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Google Scholar
- Anbarjafari, G. and Demirel, H. 2010. Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image. ETRI Journal , Issue 3, Vol. 32, pp. 390--394.Google Scholar
- Bannore, V. and Swierkowski, L. 2007. Fast Iterative Super-Resolution for Image Sequences.Digital Image Computing Techniques and Application , 9th Biennial Conference of the Australian Pattern Recognition Society on. PP.286--293 (Dec 3--5 2007). Google ScholarDigital Library
- Fanaswala,M.H. 2009. Regularized Super-Resolution of Multi-View Images.Master Thesis.Applied Science in Electrical Engineering . Carleton University (August 2009).Google Scholar
- Bannore,V.2009. Iterative-Interpolation Super-Resolution Image Reconstruction. Springer, pp. 19--50. Google ScholarDigital Library
- Gilman, A. 2009. Least-squares optimal interpolation for direct image super-resolution. Doctoral Thesis, Massey University, Palmerston North New Zealand.Google Scholar
- Zibetti, M.V.W., Bazan, F.S.V. and Mayer, J. 2008. Determining the regularization parameters for super-resolution problems. Signal Processing, Issue 12, Vol. 88, pp. 2890--2901. Google ScholarDigital Library
- Sun, J., Xu, Z. and Shum, H.Y. 2008. Image super-resolution using gradient profile prior.Computer Vision and Pattern Recognition .IEEE Conference on . pp. 1--8 (June 23--28, 2008).Google Scholar
- Freeman, W.T. and Pasztor, E.C. 1999. Learning low-level vision. Computer Vision,.The Proceedings of the Seventh IEEE International Conference on, Vol. 2, pp. 1182--1189. Google ScholarDigital Library
- Bilgazyev, E., et al. 2011. Sparse Representation-based Super-Resolution for Face Recognition At a Distance. Proceedings of the British Machine Vision Conference. pp. 52.1--52.11.Google ScholarCross Ref
- Zeyde, R., Protter, M. and Elad, M. 2010. On Single Image Scale-Up using Sparse-Representation. Springer, Curves and Surfaces. pp. 711--730. Google ScholarDigital Library
- Xiaoqing, Shutao Li. 2011. Multi-frame Image Super-resolution Reconstruction based on Sparse Representation and POCS. International Journal of Digital Content Technology and its Applications, (August 2011) , Issue Number 8, Vol.5.Google Scholar
- Elad, M., Sparse and redundant representations: from theory to applications in signal and image processing. Springer Verlag, 2010. Google ScholarDigital Library
- Aharon, M., Elad, M. and Bruckstein, A. 2006. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation. Signal Processing, IEEE Transactions on, Issue 11, Vol. 54, pp. 4311--4322 (Nov.2006). Google ScholarDigital Library
- He, J.and Zhang,D. 2010. Face super-resolution reconstruction and recognition from low-resolution image sequences. Computer Engineering and Technology (ICCET),2nd International Conference on , Vol. 2, pp. V2--620 (April 16--18, 2010).Google Scholar
- Al-Azzeh, M., Eleyan, A. and Demirel, H. 2008. PCA-based face recognition from video using super-resolution.Computer and Information Sciences.ISCIS 08 .23rd International Symposium on, pp. 1--4 (Oct. 2008).Google Scholar
- Hennings-Yeomans, P.H., Baker, S. and Kumar, B.V. 2008. Simultaneous super-resolution and feature extraction for recognition of low-resolution faces. In proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 1--8.Google Scholar
- Hennings-Yeomans, P.H., Baker, S. and Kumar, B.V.K.V. 2008. Recognition of Low-Resolution Faces Using Multiple Still Images and Multiple Cameras.Biometrics:Theory ,Application and Systems. 2nd IEEE International Conference on , pp. 1--6 (Sept 2008).Google Scholar
- Arachchige, S.R.B. 2008. Face Recognition in Low Resolution Video Sequences using Super Resolution.Master Thesis. Rochester Institute of Technology. (August 2008).Google Scholar
- Nguyen, K., Fookes, C., Sridharan, S. and Denman, S. 2011. Feature-domain super-resolution for iris recognition. Image Processing (ICIP), 18th IEEE International Conference on, pp. 3197--3200 (Sept 2011).Google Scholar
- Irani, M and Peleg,S. 1991. Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, Issue 3, Vol. 53, pp. 231--239. Google ScholarDigital Library
- Peleg, S., Keren, D. and Schweitzer, L. 1987. Improving image resolution using subpixel motion. Pattern Recognition Letters, Issue 3, Vol. 5, pp. 223--226. Google ScholarDigital Library
- Yang, J., et al. 2010. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, Issue 11, Vol. 19, pp. 2861--2873. Google ScholarDigital Library
- Yang, J., et al. 2008. Face hallucination via sparse coding. Image Processing,15th IEEE International Conference on . pp. 1264--1267 (Oct. 12-15,2008).Google Scholar
- Rubinstein, R., Bruckstein, A.M. and Elad, M. 2010. Dictionaries for sparse representation modeling. Proceedings of the IEEE, Issue 6, Vol. 98, pp. 1045--1057.Google ScholarCross Ref
- Lee, H., et al. 2007. Efficient sparse coding algorithms. Advances in neural information processing systems. Vol. 19, p. 801.Google Scholar
- Sellahewa, H. and Jassim, S.A. 2009. Image quality-based adaptive illumination normalisation for face recognition. Proceeding of SPIE , Vol. 7306, p. 73061V.Google Scholar
- Sellahewa, H. and Jassim, S.A. 2010. Image-Quality-Based Adaptive Face Recognition. Instrumentation and Measurement, IEEE Transactions on, Issue 4, Vol. 59, pp. 805--813 (April 2010).Google Scholar
- Enhancing face recognition at a distance using super resolution
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