Skip to main content
Log in

Face recognition using localized features based on non-negative sparse coding

Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Neural networks in the visual system may be performing sparse coding of learnt local features that are qualitatively very similar to the receptive fields of simple cells in the primary visual cortex, V1. In conventional sparse coding, the data are described as a combination of elementary features involving both additive and subtractive components. However, the fact that features can ‘cancel each other out’ using subtraction is contrary to the intuitive notion of combining parts to form a whole. Thus, it has recently been argued forcefully for completely non-negative representations. This paper presents Non-Negative Sparse Coding (NNSC) applied to the learning of facial features for face recognition and a comparison is made with the other part-based techniques, Non-negative Matrix Factorization (NMF) and Local-Non-negative Matrix Factorization (LNMF). The NNSC approach has been tested on the Aleix–Robert (AR), the Face Recognition Technology (FERET), the Yale B, and the Cambridge ORL databases, respectively. In doing so, we have compared and evaluated the proposed NNSC face recognition technique under varying expressions, varying illumination, occlusion with sunglasses, occlusion with scarf, and varying pose. Tests were performed with different distance metrics such as the L 1-metric, L 2-metric, and Normalized Cross-Correlation (NCC). All these experiments involved a large range of basis dimensions. In general, NNSC was found to be the best approach of the three part-based methods, although it must be observed that the best distance measure was not consistent for the different experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Hubel D.H., Wiesel T.N. (1968) Receptive fields and functional architecture of monkey striate cortex. J. Physiol. 195, 215–243

    Google Scholar 

  2. Olshausen A., Field D.J. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609

    Article  Google Scholar 

  3. Bell A.J., Sejnowski T.J. (1997) The “Independent Components” of natural scenes are edge filters. Vis. Res. 37: 3327–3338

    Article  Google Scholar 

  4. Lee D.D., Seung H.S. (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401,788–791

    Article  Google Scholar 

  5. Hoyer, P.O.: Non-Negative Sparse Coding. In: Proceedings of IEEE Workshop on Neural Networks for Signal Processing, pp. 557–565 (2002)

  6. Li S.Z., Hou X.W., Zhang H.J., Cheng Q.S. (2001) Learning spatially localized, parts-based representation. IEEE Comput. Vis. Pattern Recognit. 1, 207–212

    Google Scholar 

  7. Martinez A., Benavente R. The AR face database. Technical report 24, Computer Vision Center (CVC), Barcelona, Spain (1998)

  8. Phillips P.J., Wechsler H., Huang J., Rauss P. (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16, 295–306

    Article  Google Scholar 

  9. Georghiades A.S., Belhumeur P.N., Kriegman D.J. (2001) From few to many: illumination cones models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23, 643–660

    Article  Google Scholar 

  10. ORL Database of Faces. AT&T Laboratories, Cambridge, UK. Web address: http://www.orl.co.uk/facedatabase.html

  11. Hoyer, P.O.: Probabilistic Models of Early Vision. Ph.D. Dissertation, Department of Computer Science, Helsinki University of Technology, Finland (2002)

  12. Hafed Z.M., Levine M.D. (2001) Face recognition using the discrete cosine transform. Int. J. Comput. Vis. 43, 167–188

    Article  MATH  Google Scholar 

  13. Gandhi M. Aging adult human faces. M. Eng. Thesis, Dept. Elect. Eng., McGill University, Montreal, Canada (2004)

  14. Guillamet, D., Vitria, J.: Determining a Suitable Metric when using Non-Negative Matrix Factorization. In: Proceedings of the 16th International Conference Pattern Recognition, vol. 2, pp. 128–131 (2002)

  15. Penev P.S., Atick J.J. (1996) Local feature analysis: a general statistical theory for object representation. Neural Syst. 7, 477–500

    Article  MATH  Google Scholar 

  16. Moghaddam B., Pentland A.P. (1997) Probabilistic visual learning of object representation. IEEE Trans. Pattern Anal. Mach. Intell. 19, 696–710

    Article  Google Scholar 

  17. Turk, M.A., Pentland, A.P.: Face Recognition using Eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 586–591 (1991)

  18. Gross, R., Shi, J., Cohn, J.F.: Quo vadis Face Recognition?, Technical Report, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA (2001)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bhavin J. Shastri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shastri, B.J., Levine, M.D. Face recognition using localized features based on non-negative sparse coding. Machine Vision and Applications 18, 107–122 (2007). https://doi.org/10.1007/s00138-006-0052-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-006-0052-0

Keywords

Navigation