SEMD Based Sparse Gabor Representation for Eyeglasses-Face Recognition

  • Caifang Song
  • Baocai Yin
  • Yanfeng Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6754)


Sparse representation for face recognition has been exploited in past years. Several sparse representation algorithms have been developed. In this paper, a novel eyeglasses-face recognition approach, SEMD Based Sparse Gabor Representation, is proposed. Firstly, for a robust representation to misalignment, a sparse Gabor representation is proposed. Secondly, spatially constrained earth mover’s distance is employed instead of Euclidean distance to measure the similarity between original data and reconstructed data. The proposed algorithm for eyeglasses-face recognition has been evaluated under different eyeglasses-face databases. The experimental results reveal that the proposed approach is validity and has better recognition performance than that obtained using other traditional methods.


Eyeglasses-face recognition Sparse Gabor Representation Spatially EMD Virtual sample library 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Belhumeur, P.N.: Ongoing Challenges in Face Recognition. In: Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium, pp. 5–14 (2005)Google Scholar
  2. 2.
    Saito, Y., Kenmochi, Y., Kotani, K.: Extraction of a Symmetric Object for Eyeglass Face Analysis Using Active Contour Model. In: International Conference on Image Processing, Vancouver, Canada, vol. II, pp. 231–234 (September 2000)Google Scholar
  3. 3.
    Jing, Z., Mariani, R.: Glasses Detection and Extraction by Deformable Contour. In: Proc. Int’l Conf. Pattern Recognition, pp. 933–936 (August 2000)Google Scholar
  4. 4.
    Wu, H., et al.: Glasses Frame Detection with 3D Hough Transform. In: Proc. Int’l Conf. Pattern Recognition, pp. 346–349 (August 2002)Google Scholar
  5. 5.
    Saito, Y., Kenmochi, Y., Kotani, K.: Estimation of Eyeglassless Facial Images Using Principal Component Analysis. In: Proc. Int’l Conf. Image Processing, vol. 4, pp. 197–201 (October 1999)Google Scholar
  6. 6.
    Park, J.-S., Oh, Y.-H., Ahn, S.-C., Lee, S.-W.: Glasses Removal from Facial Image Using Recursive PCA Reconstruction. In: Proc. Int’l Conf. Audio- and Video-based Biometric Person Authentication, pp. 369–376 (June 2003)Google Scholar
  7. 7.
    Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transaction on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)CrossRefGoogle Scholar
  8. 8.
    Song, C., Yin, B., Sun, Y.: Adaptively Doubly Weighted Sub-pattern LGBP for Eyeglasses-face Recognition. Journal of Computational Information Systems 6(1), 63–70 (2010)Google Scholar
  9. 9.
    Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Ma, Y.: Towards a Practical Face Recognition System: Robust Registration and Illumination by Sparse Representation. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 4, pp. 597–604 (2009)Google Scholar
  10. 10.
    Huang, J., Huang, X., Metaxas, D.: Simultaneous image transformation and sparse representation recovery. In: IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, pp. 1–8 (2008)Google Scholar
  11. 11.
    Shan, S., Gao, W., Chang, Y., Cao, B., Yang, P.: Review the strength of Gabor features for face recognition from the angle of its robustness to misalignment. In: Proceedings of ICPR 2004, vol. I, pp. 338–341 (2004)Google Scholar
  12. 12.
    Xu, D., Yan, S., Luo, J.: Face recognition using spatially constrained earth mover’s distance. IEEE Trans. on Image Processing 17(11), 2256–2260 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Qiao, L., Chen, S., Tan, X.: Sparsity Preseving Projections with Applications to Face Recognition. Pattern Recognition, 331–341 (2010)Google Scholar
  14. 14.
    Nagesh, P., Li, B.: A Compressive Sensing Approach for Expressiong-Invariant Face Recognition. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1518–1525 (2009)Google Scholar
  15. 15.
    Ji, Y.F., Lin, T., Zha, H.B.: Mahalanobis distance based non-negative sparse representation for face recognition. In: ICMLA (2009)Google Scholar
  16. 16.
    Zhou, W., Ahrary, A., Kamata, S.-i.: Face Recognition using Local Quaternion Patters and Weighted Spatially constrained Earth Mover’s Distance. In: The 13th IEEE International Symposium on Consumer Eletronics, pp. 285–289 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Caifang Song
    • 1
  • Baocai Yin
    • 1
  • Yanfeng Sun
    • 1
  1. 1.Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and TechnologyBeijing University of TechnologyBeijingChina

Personalised recommendations