Skip to main content
SpringerLink
Log in
Book cover

Cross Disciplinary Biometric Systems pp 1–13Cite as

Feature Local Binary Patterns

  • Chapter

  • 939 Accesses

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 37))

Abstract

This chapter presents a Feature Local Binary Patterns (FLBP) method that encodes both local and feature information, where the feature pixels may be broadly defined by, for example, the edge pixels, the intensity peaks or valleys in an image, or new feature information derived from the local binary patterns or LBP. FLBP thus is expected to perform better than LBP for texture description and pattern recognition. For a given pixel and its nearest feature pixel, a distance vector is first formed by pointing from the given pixel to the feature pixel. A True Center (TC), which is the center pixel of a neighborhood, is then located on the distance vector by a TC parameter. A Virtual Center (VC), which replaces the center pixel of the neighborhood, is specified on the distance vector by a VC parameter. FLBP is then defined by comparing the neighbors of the true center with the virtual center. Note that when both the TC and VC parameters are zero, FLBP degenerates to LBP, which indicates that LBP is a special case of FLBP. Other special cases of FLBP include FLBP1 when the VC parameter is zero and FLBP2 when the TC parameter is zero.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  3. Banerji, S., Verma, A., Liu, C.: Novel color LBP descriptors for scene and image texture classification. In: 15th International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, Nevada, USA, July 18-21 (2011)

    Google Scholar 

  4. Costa, L.: 2D Euclidean distance transforms - a comparative survey. ACM Computing Surveys 40(1), 2:1–2:44 (2008)

    Google Scholar 

  5. Danielson, P.E.: Euclidean distance mapping. Computer Graphics and Image Processing 14(3), 227–248 (1980)

    Article  MathSciNet  Google Scholar 

  6. Gong, P., Marceau, D.J., Howarth, P.J.: A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data, remote sensing of environment. Remote Sensing of Environment 40(2), 137–151 (1992)

    Article  Google Scholar 

  7. Huang, X., Li, S.Z., Wang, Y.: Shape localization based on statistical method using extended local binary pattern. In: Third International Conference on Image and Graphics, Hong Kong, China, December 18-20, pp. 184–187 (2006)

    Google Scholar 

  8. Jin, H., Liu, Q., Lu, H., Tong, X.: Face detection using improved lbp under bayesian framework. In: Third International Conference on Image and Graphics, Hong Kong, China, December 18-20, pp. 306–309 (2006)

    Google Scholar 

  9. Kumar, V.V., Rao, N.G., Rao, A.L.: Reduced texture spectrum with lag value based image retrieval for medical images. International Journal of Future Generation Communication and Networking 2(4), 39–48 (2009)

    Google Scholar 

  10. Liu, C.: Enhanced independent component analysis and its application to content based face image retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 1117–1127 (2004)

    Article  Google Scholar 

  11. Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)

    Article  Google Scholar 

  12. Liu, C.: Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 725–737 (2006)

    Article  Google Scholar 

  13. Liu, C.: The Bayes decision rule induced similarity measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1086–1090 (2007)

    Article  Google Scholar 

  14. Liu, C.: Learning the uncorrelated, independent, and discriminating color spaces for face recognition. IEEE Transactions on Information Forensics and Security 3(2), 213–222 (2008)

    Article  Google Scholar 

  15. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)

    Article  Google Scholar 

  16. Liu, C., Yang, J.: ICA color space for pattern recognition. IEEE Transactions on Neural Networks 20(2), 248–257 (2009)

    Article  Google Scholar 

  17. Liu, Z., Liu, C.: Fusion of the complementary discrete cosine features in the YIQ color space for face recognition. Computer Vision and Image Understanding 111(3), 249–262 (2008)

    Article  Google Scholar 

  18. Liu, Z., Liu, C.: A hybrid color and frequency features method for face recognition. IEEE Transactions on Image Processing 17(10), 1975–1980 (2008)

    Article  MathSciNet  Google Scholar 

  19. Liu, Z., Liu, C.: Fusion of color, local spatial and global frequency information for face recognition. Pattern Recognition 43(8), 2882–2890 (2010)

    Article  MATH  Google Scholar 

  20. Maurer, C., Qi, R., Raghavan, V.: A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 265–270 (2003)

    Article  Google Scholar 

  21. Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, Jerusalem, Israel, October 9-13, pp. 582–585 (1994)

    Google Scholar 

  22. Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)

    Article  Google Scholar 

  23. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)

    Article  Google Scholar 

  24. Wang, L., He, D.C.: Texture classification using texture spectrum. Pattern Recognition 23(8), 905–910 (1990)

    Article  MathSciNet  Google Scholar 

  25. Yang, J., Liu, C.: Horizontal and vertical 2DPCA-based discriminant analysis for face verification on a large-scale database. IEEE Transactions on Information Forensics and Security 2(4), 781–792 (2007)

    Article  Google Scholar 

  26. Yang, J., Liu, C.: Color image discriminant models and algorithms for face recognition. IEEE Transactions on Neural Networks 19(12), 2088–2098 (2008)

    Article  Google Scholar 

  27. Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: A novel non-statistical model for face representation and recognition. In: 10th IEEE International Conference on Computer Vision, Beijing, China, October 17-20, pp. 786–791 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Jiayu Gu & Chengjun Liu

Authors
  1. Jiayu Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengjun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiayu Gu .

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Gu, J., Liu, C. (2012). Feature Local Binary Patterns. In: Cross Disciplinary Biometric Systems. Intelligent Systems Reference Library, vol 37. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28457-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28457-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28456-4

  • Online ISBN: 978-3-642-28457-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation