Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Face Recognition Based on Binary Template Matching

  • Conference paper
Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues (ICIC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4681))

Included in the following conference series:

Abstract

In this paper, a novel face recognition method based on binary face edges is presented to deal with the illumination problem. The Binary Face Edge Map (BFEM) is extracted using the Locally Adaptive Threshold (LAT) algorithm. Based on BEFM, a new image similarity metric is proposed. Experimental results show that face recognition rates of 76.32% and 82.67% are achieved respectively on 798 AR images and 150 Yale images with changed lighting conditions and facial expression variations when one sample per subject is used as the target image. The proposed method takes less time for image matching and outperforms some existing face recognition approaches, especially in changed lighting conditions.

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

Access this chapter

Log in via an institution

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. Brunelli, R., Poggio, T.: Face Recognition: Features Versus Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1042–1052 (1993)

    Article  Google Scholar 

  2. Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)

    Article  Google Scholar 

  3. Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 131–137 (2004)

    Article  Google Scholar 

  4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)

    Article  Google Scholar 

  5. Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks 13, 1450–1464 (2002)

    Article  Google Scholar 

  6. Takacs, B.: Comparing Face Images Using the Modified Hausdorff Distance. Pattern Recognition 31, 1873–1881 (1998)

    Article  Google Scholar 

  7. Gao, Y., Leung, M.K.H.: Line Segment Hausdorff Distance on Face Matching. Pattern Recognition 35, 361–371 (2002)

    Article  MATH  Google Scholar 

  8. Wiskott, L., Fellous, J.-M., Kruger, N., Malsburg, C.V.D.: Face Recognition by Elastic Bunch Graph Matching. Internal Report 96-08, Ruhr University, Bochum, Germany (1996)

    Google Scholar 

  9. Deniz, O., Castrillon, M., Hernandez, M.: Face Recognition Using Independent Component Analysis and Support Vector Machines. Pattern Recognition Letters 24, 2153–2157 (2003)

    Article  Google Scholar 

  10. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From Few to Many: Illumination Cone Models for Face Recognition under Differing Pose and Lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 643–660 (2001)

    Article  Google Scholar 

  11. Zhang, L., Samaras, D.: Face Recognition Under Variable Lighting Using Harmonic Image Exemplars. In: Proc. Int’l Conf. Computer Vision and Pattern Recognition, Madison, Wisconsin, vol. 1, pp. 19–25 (2003)

    Google Scholar 

  12. Wang, H., Li, S.Z., Wang, Y.: Generalized Quotient Image. In: Proc. Int’l Conf. Computer Vision and Pattern Recognition, Washington DC, vol. 2, pp. 498–505 (2004)

    Google Scholar 

  13. Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.L.: Corefaces—Robust Shift Invariant PCA Based Correlation Filter for Illumination Tolerant Face Recognition. In: Proc. Int’l Conf. Computer Vision and Pattern Recognition, Washington DC, vol. 2, pp. 834–841 (2004)

    Google Scholar 

  14. Lee, S.Y., Ham, Y.K., Park, R.H.: Recognition of Human Front Faces Using Knowledge-Based Feature Extraction and Neuro-fuzzy Algorithm. Pattern Recognition 29, 1863–1876 (1996)

    Article  Google Scholar 

  15. Yale University: http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  16. Martinez, A.M., Benavente, R.: The AR Face Database. CVC Technical Report #24, (June 1998)

    Google Scholar 

  17. Bian, Z.Q., Zhang, X.G.: Pattern Recognition (in Chinese), 2nd edn. Tsinghua University Press, Beijing (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Electronic and Information Engineering, Ningbo University of Technology, Ningbo 315016, P.R. China

    Jiatao Song, Beijing Chen, Xuena Qiu & Wei Wang

  2. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong

    Zheru Chi

Authors
  1. Jiatao Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Beijing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zheru Chi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuena Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

De-Shuang Huang Laurent Heutte Marco Loog

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Song, J., Chen, B., Chi, Z., Qiu, X., Wang, W. (2007). Face Recognition Based on Binary Template Matching. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_115

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74171-8_115

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74170-1

  • Online ISBN: 978-3-540-74171-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation