2D-DCM Based Face Specific Markov Network for Face Sketch Synthesis

Authors

  • Iswarya Manoharan  Department of Computer Science and Engineering, R.M.K Engineering College, Chennai, Tamil Nadu, India
  • Kavitha P  Department of Computer Science and Engineering, R.M.K Engineering College, Chennai, Tamil Nadu, India

Keywords:

Direct Combined Model (DCM), Canonical Correlation Analysis (CCA), Markov Random Field (MRF)

Abstract

A feature based two dimensional direct combined models for the proposed facial sketch synthesis is given by Markov network. The 2DDCM approach for the global module it presents the images to synthesis which creates an appearance of the surface as texture and global facial geometry of the input image. By using a parametric 2DDCM model and a non-parametric Markov random field, a part distinct from the whole texture is appended to the synthesized sketch in a local patch manner. As the outcome, the resemblance between the synthesized sketches and the input images is improved to the extended. At last by adding strong lines or curves to emphasize the lighting conditions to the post-processing operation. For the performance to improve the shadowed regions of the synthesized image. Pertaining to confirm that the synthesized facial images are in well quantitative and qualitative concord with the input images as the direct observation on condition by the same artist.

References

  1. Anand B. Bhanu, “Face image super-resolution using 2D CCA, ”Signal Processing, 2013, pp. 184-194.
  2. I. Berger, A. Shamir, M. Mahler, E. Carter, J. Hodgins, “Style and abstraction in portrait sketching,” ACM Transactions on Graphics (TOG), 2013, vol.32, issue 4.
  3. H. Chan, Y. Xu, H. Shum, S. Zhu, N. Zheng, “Example-based facial sketch generation with non-parametric sampling,” 8th IEEE International Conference on Computer Vision (ICCV), 2001, vol.2, pp.433-438.
  4. W.T. Freeman, E.C. Pasztor, and O.T. Carmichael, “Learning low-level vision,” Int’l J. Computer Vision, 2000, pp. 25-47
  5. X. Gao, N. Wang, D. Tao and X. Li, “Face sketch-photo synthesis and retrieval using sparse representation,” IEEE Trans. Circuits Syst. Video Technol., 2012, vol.22, pp.1213-1226.
  6. X. Gao, J. Zhong, J. Li and C. Tian, “Face sketch synthesis using E-HMM and selective ensemble,” IEEE Trans. Circuits Syst. Video Technol., 2008, vol.18, pp.487-496.
  7. X. Gao, J. Zhong, D. Tao and X. Li, “Local face sketch synthesis learning,” Neurocomputing, 2008, vol.71, no.10-12, pp.1921-1930.
  8. E. S. L. Gastal and M. M. Oliveira, “Domain transform for edge-aware image and video processing,” ACM Trans. Graph., 2011, vol.30, no.4, pp.69:1-69:12.
  9. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” Tech. Report 07-49, Univ. of Mass., 2007.
  10. X. Huang, Z. Lei, M. Fan, X. Wang, and S. Z. Li. “Regularized discriminative spectral regression method for heterogeneous face matching”, IEEE Transaction on Image Processing, 2013, pp. 353-362.
  11. B. F. Klare and A. K. Jain, “Heterogeneous face recognition using kernel prototype similarities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, vol 35, no. 6, pp.1410-1422.
  12. Q. Liu, X. Tang, H. Jin, H. Lu and S. Ma, “A nonlinear approach for face sketch synthesis and recognition,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2005, pp.1005-1010.
  13. W. Liu, X. Tang and J. Liu, “Bayesian tensor inference for sketch-based face photo hallucination,” Proc. 20th Int. Joint Conf. Artif. Intell., 2007, pp.2141-2146.
  14. C. Lu, L. Xu, and J. Jia, “Combining sketch and tone for pencil drawing production,” in Proceedings of the Symposium on Non-Photorealistic Animation and Rendering (NPAR '12), 2012, pp.6573.
  15. A. R. Martinez and R. Benavente, “The AR face database,” Technical report, CVC Technical Report No.24, June 1998.
  16. K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, “XM2VTSDB: the extended of M2VTS database,” International Conference on Audio and Video-Based Person Authentication, 1999, pp. 72-77.
  17. Y. Song, L. Bao, Q. Yang, M. Yang, “Real-time exemplar-based face sketch synthesis,” ECCV, 2014, pp.800-813.
  18. M. Song, C. Chen, J. Bu, and T. Sha, “Image-based facial sketch-to-photo synthesis via online coupled dictionary learning,” Information Sciences, 2012, pp. 223-246.
  19. X. Tang and X. Wang, “Face sketch synthesis and recognition,” Proc.IEEE Int. Conf. Comput. Vis., 2003, pp.687-694.
  20. X. Tang and X. Wang, “Face sketch recognition,” IEEE Trans. Circuits Syst. Video Technol., 2004, vol.14, pp.1-7.
  21. C. T. Tu, K. H. Lin, and T. L. Liu, “A two-dimensional direct combined model for facial hallucination,” IAPR International Conference on Machine Vision Application, 2013, pp.149-152.
  22. C. T. Tu and J. J. Lien, “Automatic location of facial feature points and synthesis of facial sketches using direct combined model,” IEEE Trans.on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, Vol.40,No.4, pp. 1158-1169, 2010.
  23. N. Wang, J. Li, D. Tao, X. Li and X. Gao, “Heterogeneous image transformation,” Pattern Recognit. Lett., 2013, vol.34, no.1, pp.77-84.
  24. N. Wang, D. Tao, X. Gao, X. Li, J. Li, “Transductive face sketch-photo synthesis,” IEEE Transactions on Neural Networks and Learning Systems, 2013, pp. 1364-1376.
  25. N. Wang, D. Tao, X. Gao, X. Li, J. Li, “A comprehensive survey to face hallucination,” International Journal of Computer Vision, 2014, pp. 9-30.
  26. S. Wang, L. Zhang, Y. Liang and Q. Pan, “Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2012,pp.2216-2223.
  27. X. Wang and X. Tang, “Face photo-sketch synthesis and recognition, ”IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2009, vol.31, no.11, pp.1955-1967.
  28. B. Xiao, X. Gao, D. Tao, Y. Yuan and J. Li, “Photo-sketch synthesis and recognition based on subspace learning,” Neurocomputing, 2010, vol.73, no.4-6, pp.840-852.
  29. B. Xiao, X. Gao, D. Tao and X. Li, “A new approach for face recognition by sketches in photos,” Signal Process., 2009, vol.89, no.8, pp.1576-1588.
  30. Z. Xu, H. Chen, S.C. Zhu and J. Luo, “A hierarchical compositional model for face representation and sketching,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2008, vol.30, no.6, pp.955-969.
  31. W. Yang, D. Yi, Z. Lei, J. Sang, and S. Li, “2D–3D face matching using CCA,” IEEE Conference on Automatic Face Gesture Recognition, 2008,pp.1-6.
  32. W. Zhang, X. Wang, and X. Tang, “Lighting and pose robust face sketch synthesis,” ECCV, 2010, pp.420-433.
  33. S. Zhang, X. Gao, N. Wang, J. Li, and M. Zhang, “Face sketch synthesis via sparse representation-based greedy search,” IEEE Transactions on Image Processing, 2015, vol. 24, no. 8, pp. 2466-2477.
  34. H. Zhou, Z. Kuang and K. Wong, “Markov weight fields for face sketch synthesis,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2012,pp.1091-1097.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2017-04-30

Issue

Volume 2, Issue 2, March-April-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Iswarya Manoharan, Kavitha P, " 2D-DCM Based Face Specific Markov Network for Face Sketch Synthesis, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.1140-1146, March-April-2017.