Weight-Optimal Local Binary Patterns

  • Felix Juefei-XuEmail author
  • Marios Savvides
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)


In this work, we have proposed a learning paradigm for obtaining weight-optimal local binary patterns (WoLBP). We first re-formulate the LBP problem into matrix multiplication with all the bitmaps flattened and then resort to the Fisher ratio criterion for obtaining the optimal weight matrix for LBP encoding. The solution is closed form and can be easily solved using one eigen-decomposition. The experimental results on the FRGC ver2.0 database have shown that the WoLBP gains significant performance improvement over traditional LBP, and such WoLBP learning procedure can be directly ported to many other LBP variants to further improve their performances.


Local binary patterns (LBP) Weight-optimal local binary patterns (WoLBP) 


  1. 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) CrossRefGoogle Scholar
  2. 2.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07–49, Univ. of Massachusetts, Amherst, October 2007Google Scholar
  3. 3.
    Jin, H., Liu, Q., Lu, H., Tong, X.: Face detection using improved lbp under bayesian framework. In: Proc. 3rd Int’l Conf. on Image and Graphics, pp. 306–309, December 2004Google Scholar
  4. 4.
    Juefei-Xu, F., Cha, M., Heyman, J.L., Venugopalan, S., Abiantun, R., Savvides, M.: Robust local binary pattern feature sets for periocular biometric identification. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–8. IEEE (2010)Google Scholar
  5. 5.
    Juefei-Xu, F., Cha, M., Savvides, M., Bedros, S., Trojanova, J.: Robust periocular biometric recognition using multi-level fusion of various local feature extraction techniques. In: IEEE 17th International Conference on Digital Signal Processing (DSP) (2011)Google Scholar
  6. 6.
    Juefei-Xu, F., Luu, K., Savvides, M., Bui, T.D., Suen, C.Y.: Investigating age invariant face recognition based on periocular biometrics. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–7. IEEE (2011)Google Scholar
  7. 7.
    Juefei-Xu, F., Pal, D.K., Savvides, M.: Hallucinating the full face from the periocular region via dimensionally weighted k-svd. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW). IEEE (2014)Google Scholar
  8. 8.
    Juefei-Xu, F., Savvides, M.: Can your eyebrows tell me who you are?. In: 2011 5th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–8. IEEE (2011)Google Scholar
  9. 9.
    Juefei-Xu, F., Savvides, M.: Unconstrained periocular biometric acquisition and recognition using cots ptz camera for uncooperative and non-cooperative subjects. In: 2012 IEEE Workshop on Applications of Computer Vision (WACV), pp. 201–208. IEEE (2012)Google Scholar
  10. 10.
    Juefei-Xu, F., Savvides, M.: An augmented linear discriminant analysis approach for identifying identical twins with the aid of facial asymmetry features. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) (2013)Google Scholar
  11. 11.
    Juefei-Xu, F., Savvides, M.: An image statistics approach towards efficient and robust refinement for landmarks on facial boundary. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2013)Google Scholar
  12. 12.
    Juefei-Xu, F., Savvides, M.: Facial ethnic appearance synthesis. In: European Conference on Computer Vision (ECCV) Workshops. Springer (2014)Google Scholar
  13. 13.
    Juefei-Xu, F., Savvides, M.: Subspace based discrete transform encoded local binary patterns representations for robust periocular matching on nists face recognition grand challenge. IEEE Transactions on Image Processing (2014)Google Scholar
  14. 14.
    Lei, Z., Pietikainen, M., Li, S.: Learning discriminant face descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(2), 289–302 (2014)CrossRefGoogle Scholar
  15. 15.
    Lei, Z., Yi, D., Li, S.: Discriminant image filter learning for face recognition with local binary pattern like representation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2512–2517, June 2012Google Scholar
  16. 16.
    Leibo, J.Z., Liao, Q., Poggio, T.: Subtasks of unconstrained face recognition. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2014)Google Scholar
  17. 17.
    Liao, S.C., Zhu, X.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) CrossRefGoogle Scholar
  18. 18.
    Maturana, D., Mery, D., Soto, A.: Learning discriminative local binary patterns for face recognition. In: 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011), pp. 470–475, March 2011Google Scholar
  19. 19.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  20. 20.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition CVPR. vol. 1, pp. 947–954, June 2005Google Scholar
  21. 21.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)Google Scholar
  22. 22.
    Savvides, M., Juefei-Xu, F.: Image matching using subspace-based discrete transform encoded local binary patterns (09 2013).
  23. 23.
    Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters 33, 431–437 (2012)CrossRefGoogle Scholar
  24. 24.
    Shan, C., Gong, S., McOwan, P.W.: Robust facial expression recognition using local binary patterns. In: IEEE Int’l Conf. on Image Processing ICIP. vol. 2, pp. 370–376, September 2005Google Scholar
  25. 25.
    Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L.: Gender classification based on boosting local binary pattern. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 194–201. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  26. 26.
    Sun, Z., Tan, T., Qiu, X.: Graph matching iris image blocks with local binary pattern. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 366–372. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  27. 27.
    Zhang, H., Zhao, D.: Spatial histogram features for face detection in color images. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3331, pp. 377–384. Springer, Heidelberg (2004) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Carnegie Mellon UniversityPittsburghUSA

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