About this Journal Submit a Manuscript Table of Contents
ISRN Machine Vision
Volume 2012 (2012), Article ID 976160, 13 pages
http://dx.doi.org/10.5402/2012/976160
Research Article

A Wavelet-Domain Local Dominant Feature Selection Scheme for Face Recognition

Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh

Received 22 November 2011; Accepted 13 December 2011

Academic Editor: A. Lanitis

Copyright © 2012 Hafiz Imtiaz and Shaikh Anowarul Fattah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4–20, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Villegas-Quezada and J. Climent, “Holistic face recognition using multivariate approximation, genetic algorithms and adaboost classifier: preliminary results,” World Academy of Science, Engineering and Technology, vol. 44, pp. 802–806, 2008.
  3. L. Shen and L. Bai, “Gabor feature based face recognition using kernel methods,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FGR '04), vol. 6, pp. 386–389, 2004. View at Scopus
  4. M. Zhou and H. Wei, “Face verification using gabor wavelets and AdaBoost,” in Proceedings of the 18th International Conference on Pattern Recognition (ICPR '06), vol. 1, pp. 404–407, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Tan, S. Chen, Z. H. Zhou, and F. Zhang, “Face recognition from a single image per person: a survey,” Pattern Recognition, vol. 39, no. 9, pp. 1725–1745, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Gao and M. K. H. Leung, “Face recognition using line edge map,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 764–779, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. C. BenAbdelkader and P. Griffin, “A local region-based approach to gender classification from face images,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 3, pp. 52–57, 2005.
  8. T. Ahonen, A. Hadid, and M. Pietikäinen, “Face description with local binary patterns: application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  9. R. Gottumukkal and V. K. Asari, “An improved face recognition technique based on modular PCA approach,” Pattern Recognition Letters, vol. 25, no. 4, pp. 429–436, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. C. C. Liu and D. Q. Dai, “Face recognition using dual-tree complex wavelet features,” IEEE Transactions on Image Processing, vol. 18, no. 11, pp. 2593–2599, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  11. H. Imtiaz and S. Fattah, “A face recognition scheme using wavelet-based local features,” in Proceedings of the IEEE Symposium on Computers and Informatics (ISCI '11), pp. 313–316, 2011.
  12. H. Imtiaz and S. Fattah, “A wavelet-domain local feature selection scheme for face recognition,” in Proceedings of the International Conference on Communication and Signal Processing (ICCSP '11), pp. 448–451, 2011.
  13. Y. Utsumi, Y. Iwai, and M. Yachida, “Performance evaluation of face recognition in the wavelet domain,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '06), pp. 3344–3351, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Alirezaee, H. Aghaeinia, K. Faez, and F. Askari, “An efficient algorithm for face localization,” International Journal of Information Technology, vol. 12, pp. 30–36, 2006.
  15. E. Loutas, I. Pitas, and C. Nikou, “Probabilistic multiple face detection and tracking using entropy measures,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 128–135, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. S. C. Dakin and R. J. Watt, “Biological ‘bar codes’ in human faces,” World Academy of Science, Engineering and Technology, vol. 9, pp. 1–10, 2009.
  17. X. Zhang and Y. Gao, “Face recognition across pose: a review,” Pattern Recognition, vol. 42, no. 11, pp. 2876–2896, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. I. Jolloffe, Principal Component Analysis, Springer, Berlin, Germany, 1986.
  19. F. M. D. S. Matos, L. V. Batista, and J. V. D. Poel, “Face recognition using DCT coefficients selection,” in Proceedings of the 23rd Annual ACM Symposium on Applied Computing (SAC '08), pp. 1753–1757, March 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Y. Jing and D. Zhang, “A face and palmprint recognition approach based on discriminant DCT feature extraction,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 34, no. 6, pp. 2405–2415, 2004. View at Publisher · View at Google Scholar · View at Scopus