Low Resolution Sparse Binary Face Patterns

Swathikiran Sudhakaran, Alex Pappachen James

Abstract

Automated recognition of low resolution face images from thumbnails represent a challenging image recognition problem. We propose the sequential fusion of wavelet transform computation, local binary pattern and sparse coding of images to accurately extract facial features from thumbnail images. A minimum distance classifier with Shepard's similarity measure is used as the classifier. The proposed method shows robust recognition performance when tested on face datasets (Yale B, AR and PUT) when compared against benchmark techniques for very low resolution (i.e. less than 45x45 pixels) face image recognition. The possible applications of the proposed thumbnail recognition include contextual search, intelligent image/video sorting and groups, and face image clustering.

References

  1. Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 28(12):2037-2041.
  2. Baker, S. and Kanade, T. (2000). Hallucinating faces. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on, pages 83-88. IEEE.
  3. Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21-27.
  4. Field, D. J. (1999). Wavelets, vision and the statistics of natural scenes. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 357(1760):2527-2542.
  5. Freeman, W. T., Pasztor, E. C., and Carmichael, O. T. (2000). Learning low-level vision. International journal of computer vision, 40(1):25-47.
  6. Gao, R. X. and Yan, R. (2010). Wavelets: Theory and applications for manufacturing. Springer Science & Business Media.
  7. Kasinski, A., Florek, A., and Schmidt, A. (2008). The put face database. Image Processing and Communications, 13(3-4):59-64.
  8. Kim, K. I., Jung, K., and Kim, H. J. (2002). Face recognition using kernel principal component analysis. Signal Processing Letters, IEEE, 9(2):40-42.
  9. Lai, J. and Jiang, X. (2012). Modular weighted global sparse representation for robust face recognition. Signal Processing Letters, IEEE, 19(9):571-574.
  10. Lee, K., Ho, J., and Kriegman, D. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intelligence, 27(5):684-698.
  11. Li, B., Chang, H., Shan, S., and Chen, X. (2010). Lowresolution face recognition via coupled locality preserving mappings. Signal Processing Letters, IEEE, 17(1):20-23.
  12. Liu, Q., Huang, R., Lu, H., and Ma, S. (2002). Face recognition using kernel-based fisher discriminant analysis. In Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on, pages 197-201. IEEE.
  13. Mallat, S. (1999). A wavelet tour of signal processing. Academic Press.
  14. Marciniak, T., Dabrowski, A., Chmielewska, A., and Weychan, R. (2012). Face recognition from low resolution images. In Multimedia Communications, Services and Security, pages 220-229. Springer.
  15. Martinez, A. M. (1998). The ar face database. CVC Technical Report, 24.
  16. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971-987.
  17. Patel, V. M., Chen, Y.-C., Chellappa, R., and Phillips, P. J. (2014). Dictionaries for image and video-based face recognition. J. Opt. Soc. Am. A, 31(5):1090-1103.
  18. Pietikäinen, M. (2010). Local binary patterns. Scholarpedia, 5(3):9775.
  19. Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science, 237(4820):1317-1323.
  20. Sudhakaran, S. and James, A. P. (2015). Sparse distributed localized gradient fused features of objects. Pattern Recognition, 48(4):1534-1542.
  21. Turk, M. A. and Pentland, A. P. (1991). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on, pages 586-591. IEEE.
  22. Xu, X., Liu, W., and Li, L. (2014). Low resolution face recognition in surveillance systems. Journal of Computer and Communications, 2(02):70.
  23. Zhang, B.-L., Zhang, H., and Ge, S. S. (2004). Face recognition by applying wavelet subband representation and kernel associative memory. Neural Networks, IEEE Transactions on, 15(1):166-177.
  24. Zou, W. and Yuen, P. (2010). Very low resolution face recognition problem. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on, pages 1-6.
Download


Paper Citation


in Harvard Style

Sudhakaran S. and James A. (2016). Low Resolution Sparse Binary Face Patterns . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 186-191. DOI: 10.5220/0005782601860191


in Bibtex Style

@conference{visapp16,
author={Swathikiran Sudhakaran and Alex Pappachen James},
title={Low Resolution Sparse Binary Face Patterns},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005782601860191},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Low Resolution Sparse Binary Face Patterns
SN - 978-989-758-175-5
AU - Sudhakaran S.
AU - James A.
PY - 2016
SP - 186
EP - 191
DO - 10.5220/0005782601860191