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Application of Blind Deblurring Algorithm for Face Biometric

by F.alaoui, G. Abdulatef Abdo, V.dembele, A.nassim
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 2
Year of Publication: 2014
Authors: F.alaoui, G. Abdulatef Abdo, V.dembele, A.nassim
10.5120/18349-9473

F.alaoui, G. Abdulatef Abdo, V.dembele, A.nassim . Application of Blind Deblurring Algorithm for Face Biometric. International Journal of Computer Applications. 105, 2 ( November 2014), 20-24. DOI=10.5120/18349-9473

@article{ 10.5120/18349-9473,
author = { F.alaoui, G. Abdulatef Abdo, V.dembele, A.nassim },
title = { Application of Blind Deblurring Algorithm for Face Biometric },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 2 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number2/18349-9473/ },
doi = { 10.5120/18349-9473 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:36:37.920479+05:30
%A F.alaoui
%A G. Abdulatef Abdo
%A V.dembele
%A A.nassim
%T Application of Blind Deblurring Algorithm for Face Biometric
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 2
%P 20-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition is one of the most important abilities which we use in public security and for identity verification for physical and logical access. It is well known that many image-based face recognition algorithms perform well, when constrained (frontal, well illuminated, high-resolution, sharp, and full) face images are acquired. However, their performance degrades signi?cantly when the test images contain variations that are not present in the training images. Face recognition in constrained acquisition conditions is one of the most challenging problems that have been actively researched in recent years. There are many factors low resolution, poor illumination, pose variation, occlusion and relative motion between the sensor and objects in the scene to substantially degrade performance more than the other quality. The work described in this paper is interested in Motion Blur. Motion Blur is often present in real-world images and signi?cantly affects the performance of face recognition systems. This paper proposes a novel application for recognizing faces degraded by blur using deblurring of facial images using fast TV-l1 deconvolution model. Experiments on a face database (FERET) arti?cially degraded by motion blur show that the faces recognition accuracy was better than that when using debluring algorithms.

References
  1. K. Fukunaga. Introduction to statistical patter recognition. Academic Press, Boston, 2 edition, 1990
  2. Ahonen, T. , Hadid, A. , Pietikainen, M. : Face recognition with local binary patterns. In: Proceedings of the European Conference on Computer Vision, pp. 469–481. Prague, Czech Republic (2004)
  3. T. Ahonen, E. Rahtu, V. Ojansivu, and J. Heikkila. Recognition of blurred faces using local phase quantization. In ICPR, pages 1–4, Dec. 2008
  4. H. Andrews and B. Hunt. Digital Image Restoration. Prentice Hall Signal Processing Series, 1977
  5. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. Of IEEE Conference on Computer vision and pattern Recognition, Kauai, Hi, Decembre 2001
  6. P. J. Phillips, H. Moon, P. Rauss, and S. Rizvi. The FERET evaluation methodology for face- recognition algorithms. In Proceedings of IEEE Computer Vision and Pattern Recognition, pages 137–143, June 1997.
  7. M. Nishiyama, H. Takeshima, J. Shotton, T. Kozakaya, and O. Yamaguchi. Facial deblur inference to improve recognition of blurred faces. Proc. CVPR, pages 1115– 1122, 2009
  8. Stan Z. Li and Anil K. Jain "Handbook of Face Recognition", Springer
  9. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. ACM Transactions on Graphics, 25:787–794, Mar. 2006.
  10. J. Flusser, J. Boldys, and and B. Zitova. Moment forms invariant to rotation and blur in arbitrary number of dimensions. IEEE TPAMI, 25:234–246, Feb. 2003.
  11. J. Flusser, J. Boldy. and Zitov. Invariants to convolution in arbitrary dimensions. Journal of Mathematical Imaging and Vision, 13:101–113, Feb. 2000.
  12. J. Flusser and T. Suk. Degraded image analysis: an invariant approach. IEEE TPAMI, 20:590 –603, June 1998.
  13. J. Flusser, T. Suk, and S. Saic. Recognition of images degraded by linear motion blur without restoration. In Proc. Theoretical Foundations of Computer Vision, pages 37–51, Sep. 1996.
  14. S. Z. Li and A. K. Jain,editors. Handbook of Face Recognition. SpringerVerlag, New York, 2005
  15. A . Hadid and M. Nishiyama 'Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors'
  16. T. Kanade. Picture Processing by Computer Complex and Recognition of Human Faces. Ph. D. thesis,Kyoto University, 1973.
  17. H. Hu and G. de Haan. Low cost robust blur estimator. In ICIP, pages 617–620, Oct. 2006.
  18. R. Gopalan, P. Turaga, R. Chellappa. A Blur-robust Descriptor with Applications to Face Recognition, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE.
  19. A. Levin, D. Lischinski, and Y. Weiss. A closed-form solution to natural image matting. IEEE TPAMI, 30:228–242, Feb. 2008.
  20. A. Levin, Y. Weiss, F. Durand, and W. Freeman. Ef?cient marginal likelihood optimization in blind deconvolution. In CVPR, pages 2657 –2664, June 2011.
  21. M. Nishiyama, H. Takeshima, J. Shotton, T. Kozakaya, an O. Yamaguchi. Facial deblur inference to improve recognition of blurred faces. In CVPR, pages 1115–1122, June 2009.
  22. M. Nishiyama, A. Hadid, H. Takeshima, J. Shotton, T. Kozakaya, and O. Yamaguchi. Facial deblur inference using subspace analysis for recognition of blurred faces. IEEE TPAMI, 33:838 –845, Apr. 2011.
  23. L. Xu and Jiaya Jia, "Two-Phase Kernel Estimation for Robust Motion Deblurring", European Conference on Computer Vision (ECCV), 2010.
  24. V. Ojansivu and J. Heikkia. A method for blur and af?ne invariant object recognition using phase-only bispectrum. In ICIAR, pages 527–536, June 2008
  25. A. N. Tikhonov and V. Y. Arsenin. Solutions of Ill-Posed Problems. V. H. Winston & Sons, Washington, D. C. : John Wiley & Sons, New York,, 1977.
  26. Ojansivu, V. , Heikkilä, J. : Blur insensitive texture classification using local phase quantization. In: Proc. of the International Conference on Image and Signal Processing (2008)
  27. L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259–268, Nov. 1992.
  28. T. Suk and J. Flusser. Combined blur and af?ne moment invariants and their use in pattern recognition. Pattern Recognition, 36:2895 – 2907, Dec. 2003.
  29. I. Stainvas and N. Intrator. Blurred face recognition via a hybrid network architecture. . In ICPR, pages 805–808, Sep 2000
  30. S. Osher and L. I. Rudin. Feature-oriented image enhancement using shock filters. SIAM Journal on Numerical Analysis, 1990, 27(4):919–940.
  31. S. Cho and S. Lee. Fast motion deblurring. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) , 2009, 28(5):1–8.
  32. L. Yuan, J. Sun, L. Quan, and H. Shum. Image deblurring with blurred/noisy image pairs. ACM Transactions on Graphics (Proc. SIGGRAPH), 2007, 26(3):1–10.
  33. Y. Wang and W. Yin. Sparse signal reconstruction via iterative support detection. SIAM Journal on Imaging Sciences, 2010, 3(3):462–491.
  34. Y. Wang, J. Yang, W. Yin, and Y. Zhang. A new alternating minimization algorithm for total variation image reconstruction SIAM Journal on Imaging Sciences, 2008, 1(3):248–272.
Index Terms

Computer Science
Information Sciences

Keywords

Face Recognition Motion blur Deconvolution.

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