Skip to main content

Single Sample Face Recognition Based on DCT and Local Gabor Binary Pattern Histogram

  • Conference paper
Intelligent Computing Theories (ICIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7995))

Included in the following conference series:

Abstract

For years, researchers in face recognition area have been representing and recognizing faces based on subspace discriminant analysis or statistical learning. Nevertheless, for single sample face recognition these approaches are always suffering from the generalizability problem because of small samples. This paper proposes a novel non-statistics features extraction approach based on fusion of DCT and local Gabor binary pattern Histogram (LGBPH). The global and low frequency features are obtained by low frequency coefficients of discrete cosine transform (DCT). The local and high frequency features are extracted by LGBPH. To integrate the global and local features, the final recognition can be achieved by parallel integration of classification results of the global and local features. In DCT and LGBPH, training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided. The experimental results on ORL face databases show that the global face and local information can be integrated well after level fusion by global and local features, which improve the performance of single sample face recognition.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Tan, X., Chen, S., Zhou, Z., And Zhang, F.: Face Recognition from A Single Image per Person: A Survey. Pattern Recognition 39(9), 1725–1745 (2006)

    Article  MATH  Google Scholar 

  2. Zhao, W.Y., Chellappa, R., Phillips, P.J., Rosenfeld, A.P.: Face Recognition: a literature survey. ACM Computing Surveys 35(4), 399–458 (2003)

    Article  Google Scholar 

  3. Zhang, W., Shan, S., Gao, W., et al.: Local Gabor Binary Pattern Histogram Sequence (LGBPHS):A Novel Non-Statistical Model for Face Representation and Recognition. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV 2005), pp. 786–791 (2005)

    Google Scholar 

  4. Loog, M., Duin, R., Haeb-Umbach, R.: Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(7), 762–766 (2001)

    Article  Google Scholar 

  5. Hamsici, O.C., Martinez, A.M.: Bayes Optimality in Linear Discriminant Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(4), 647–657 (2008)

    Article  Google Scholar 

  6. Kao, W., Hsu, M., Yang, Y.: Local Contrast Enhancement And Adaptive Feature Extraction for Illumination-Invariant Face Recognition. Pattern Recognition 43(5), 1736–1747 (2010)

    Article  MATH  Google Scholar 

  7. Kan, M., Shan, S., Su, Y., Chen, X., Gao, W.: Adaptive Discriminant Analysis for Face Recognition from Single Sample Per Person. In: FG 2011, pp. 193–199 (2011)

    Google Scholar 

  8. Yu, S., Shan, S., Chen, X., et al.: Integration of Global and Local Feature for Face Recognition. Journal of Software 21(8), 1849–1862 (2010)

    Article  MATH  Google Scholar 

  9. Wang, J., Plataniotis, K.N., Lu, J., et al.: On Solving The Face Recognition Problem with One Training Sample per Subject. Pattern Recognition 39(9), 174–1762 (2006)

    Article  Google Scholar 

  10. Kou, J., Du, J.-X., Zhai, C.-M.: Integration of Global And Local Feature for Age Estimation of Facial Images. In: Huang, D.-S., Ma, J., Jo, K.-H., Gromiha, M.M. (eds.) ICIC 2012. LNCS, vol. 7390, pp. 455–462. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Lu, J., Tan, Y., Wang, G.: Discriminative Multi-Manifold Analysis for Face Recognition from A Single Training Sample per Person. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 39–51 (2013)

    Article  Google Scholar 

  12. Zhang, Y., Liu, C.: A Novel Face Recognition Method Based on Linear Discriminant Analysis. Journal of Infrared and Millimeter Waves 22(5), 327–330 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xie, Z. (2013). Single Sample Face Recognition Based on DCT and Local Gabor Binary Pattern Histogram. In: Huang, DS., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds) Intelligent Computing Theories. ICIC 2013. Lecture Notes in Computer Science, vol 7995. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39479-9_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39479-9_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39478-2

  • Online ISBN: 978-3-642-39479-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics