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International Conference on Ubiquitous Computing and Ambient Intelligence

UCAmI 2014: Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services pp 532–539Cite as

A General Weighted Multi-scale Method for Improving LBP for Face Recognition

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8867)

Abstract

LBP (Local Binary Pattern) is a popular image descriptor (feature) that has been widely used in face recognition. LBP has some parameters, and different parameter values leads to different LBP feature vectors. In practice usually only one feature vector is used for one image, thus information about image content is not utilised fully by LBP. In this paper a novel way of utilising LBP features more fully is presented. Different LBP feature vectors are extracted for one image, corresponding to different combinations of LBP parameter values. These vectors are weighted and used in a distance function. Then the k-nearest neighbour classifier is used. Experiments have been conducted on the AR database. Results show this method does indeed produce better classification performance, suggesting that more information considered this way can have values.

Keywords

  • face recognition
  • LBP
  • weighting
  • multi-scale

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  • DOI: 10.1007/978-3-319-13102-3_84
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References

  1. 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)

    CrossRef  Google Scholar 

  2. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)

    CrossRef  Google Scholar 

  3. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  4. Using Discriminant Eigenfeatures. Short paper’s. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8) (1996)

    Google Scholar 

  5. Guo, Z., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing 19(6), 1657–1663 (2010)

    CrossRef  MathSciNet  Google Scholar 

  6. Guo, Z., Zhang, D., Mou, X.: Hierarchical multiscale lbp for face and palmprint recognition. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 4521–4524. IEEE (2010)

    Google Scholar 

  7. Hertz, T., Bar-Hillel, A., Weinshall, D.: Learning distance functions for image retrieval. In: Proceedings of IEEE Conference in Computer Vision and Pattern Recognition, vol. 2 (2004)

    Google Scholar 

  8. Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  9. 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)

    CrossRef  Google Scholar 

  10. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

  11. Martinez, A., Benavente, R.: The AR Face Database. CVC Tech.Report 24, Report 24 (1998)

    Google Scholar 

  12. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing 22(10), 761–767 (2004)

    CrossRef  Google Scholar 

  13. 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)

    CrossRef  Google Scholar 

  14. Ramanan, D., Baker, S.: Local distance functions: A taxonomy, new algorithms, and an evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 3(4), 794–806 (2011)

    CrossRef  Google Scholar 

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

    Google Scholar 

  16. 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(1), 131–137 (2004)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, University of Ulster at Jordanstown, Belfast, UK

    Hui Wang

  2. Key Lab of Network Security and Cryptology, School of Mathematics and Computer Science, Fujian Normal University, P.R. China

    Xin Wei, Gongde Guo & Huan Wan

Authors
  1. Xin Wei
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  2. Hui Wang
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  3. Gongde Guo
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  4. Huan Wan
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Editor information

Editors and Affiliations

  1. Escuela Superior de Informática, Castilla Ly Mancha University, Paseo de la Universidad 4, 13071, Ciudad Real, Spain

    Ramón Hervás

  2. Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, 446-701, Yongin-si, Gyeongi-do, Korea

    Sungyoung Lee

  3. Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, Jordanstown Campus, Shore Road, BT37 0QB, Newtownabbey, UK

    Chris Nugent

  4. Escuela Superior de Informática, Castilla La Mancha University, Paseo de la Universidad 4, 13071, Ciudad Real, Spain

    José Bravo

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Wei, X., Wang, H., Guo, G., Wan, H. (2014). A General Weighted Multi-scale Method for Improving LBP for Face Recognition. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. UCAmI 2014. Lecture Notes in Computer Science, vol 8867. Springer, Cham. https://doi.org/10.1007/978-3-319-13102-3_84

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  • DOI: https://doi.org/10.1007/978-3-319-13102-3_84

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13101-6

  • Online ISBN: 978-3-319-13102-3

  • eBook Packages: Computer ScienceComputer Science (R0)