Weighted SIFT Feature Learning with Hamming Distance for Face Recognition

Guoyu Lu, Yingjie Hu, Chandra Kambhamettu

2014

Abstract

Scale-invariant feature transform (SIFT) feature has been successfully utilized for face recognition for its tolerance to the changes of image scaling, rotation and distortion. However, a big concern on the use of original SIFT feature for face recognition is SIFT feature’s high dimensionality which leads to slow image matching. Meanwhile, large memory capacity is required to store high dimensional SIFT features. Aiming to find an efficient approach to solve these issues, we propose a new integrated method for face recognition in this paper. The new method consists of two novel functional modules in which a projection function transforms the original SIFT features into a low dimensional Hamming feature space while each bit of the Hamming descriptor is ranked based on their discrimination power. Furthermore, a weighting function assigns different weights to the correctly matched features based on their matching times. Our proposed face recognition method has been applied on two benchmark facial image datasets: ORL and Yale datasets. The experimental results have shown that the new method is able to produce good image recognition rate with much improved computational speed.

References

  1. Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. TPAMI, 28(12).
  2. Belhumeur, P., Hespanha, J., and Kriegman, D. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. TPAMI, 19:711-720.
  3. Bicego, M., Lagorio, A., Grosso, E., and Tistarelli, M. (2006). On the use of sift features for face authentication. In CVPR Workshop, pages 35-41.
  4. D.Lowe (2004). Distinctive image features from scaleinvariant keypoints. IJCV, 60(2):91-110.
  5. Fernandez, C. and Vicente, M. (2008). Face recognition using multiple interest point detectors and sift descriptors. In FG.
  6. Fischler, M. A. and Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6).
  7. Gao, S., Tsang, I. W.-H., and Chia, L.-T. (2010). Kernel sparse representation for image classification and face recognition. In ECCV.
  8. Geng, C. and Jiang, X. (2011). Face recognition based on the multi-scale local image structures. Pattern Recognition, 44(10-11).
  9. Jiang, X. (2011). Linear subspace learning-based dimensionality reduction. IEEE Signal Processing Magazine, 28(2):16-26.
  10. Kriz?aj, J., S? truc, V., and Paves?ic, N. (2010). Adaptation of sift features for robust face recognition. In Image Analysis and Recognition, volume 6111, pages 394- 404.
  11. Liu, J., Li, B., and Zhang, W.-S. (2012). Feature extraction using maximum variance sparse mapping. Neural Computing and Applications.
  12. Lu, G.-F., Lin, Z., and Jin, Z. (2010). Face recognition using discriminant locality preserving projections based on maximum margin criterion. Pattern Recognition, 43(10).
  13. Luo, J., Ma, Y., Takikawa, E., Lao, S., Kawade, M., and Lu, B.-L. (2007). Person-specific sift features for face recognition. In ICASSP.
  14. Majumdar, A. and Ward, R. (2009). Discriminative sift features for face recognition. In CCECE.
  15. Mian, A. S., Bennamoun, M., and Owens, R. (2008). Keypoint detection and local feature matching for textured 3d face recognition. IJCV, 79(1).
  16. Philbin, J., Isard, M., Sivic, J., and Zisserman, A. (2010). Descriptor learning for efficient retrieval. In ECCV.
  17. Rosenberger, C. and Brun, L. (2008). Similarity-based matching for face authentication. In ICPR.
  18. Samaria, F. and Harter, A. (1994). Parameterisation of a stochastic model for human face identification. In ICCV Workshop.
  19. Soyel, H. and Demirel, H. (2011). Localized discriminative scale invariant feature transform based facial expression recognition. Computers & Electrical Engineering.
  20. Strecha, C., Bronstein, A., Bronstein, M., and Fua, P. (2012). Ldahash: Improved matching with smaller descriptors. TPAMI, 34:66-78.
  21. Strecha, C., Pylvanainen, T., and Fua, P. (2010). Dynamic and scalable large scale image reconstruction. In CVPR.
  22. Turk, M. and Pentland, A. (1991). Face recognition using eigenfaces. Cognitive Neurosicence, 3(1):71-86.
  23. Wright, J. and Hua, G. (2009). Implicit elastic matching with random projections for pose-variant face recognition. In CVPR.
  24. Yang, T. and Kecman, V. (2010). Face recognition with adaptive local hyperplane algorithm. Pattern Analysis and Applications, 13.
  25. Zhang, Q., Chen, Y., Zhang, Y., and Xu, Y. (2008). Sift implementation and optimization for multi-core systems. In IPDPS.
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Paper Citation


in Harvard Style

Lu G., Hu Y. and Kambhamettu C. (2014). Weighted SIFT Feature Learning with Hamming Distance for Face Recognition . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 691-699. DOI: 10.5220/0004859806910699


in Bibtex Style

@conference{visapp14,
author={Guoyu Lu and Yingjie Hu and Chandra Kambhamettu},
title={Weighted SIFT Feature Learning with Hamming Distance for Face Recognition},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={691-699},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004859806910699},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Weighted SIFT Feature Learning with Hamming Distance for Face Recognition
SN - 978-989-758-004-8
AU - Lu G.
AU - Hu Y.
AU - Kambhamettu C.
PY - 2014
SP - 691
EP - 699
DO - 10.5220/0004859806910699