Periocular Feature Extraction Based on LBP and DLDA

  • Akanksha Joshi
  • Abhishek Gangwar
  • Renu Sharma
  • Zia Saquib
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


Periocular recognition is an emerging field of research and people have experimented with some feature extraction techniques to extract robust and unique features from the periocular region. In this paper, we propose a novel feature extraction approach to use periocular region as a biometric trait. In this approach we first applied Local Binary Patterns (LBPs) to extract the texture information from the periocular region of the image and then applied Direct Linear Discriminant Analysis (DLDA) to produce discriminative low-dimensional feature vectors. The approach is evaluated on the UBIRIS v2 database and we achieved 94% accuracy which is a significant improvement in the performance of periocular recognition.


Periocular recognition local binary patterns direct linear discriminant analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Park, U., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum: A feasibility study. In: Proc. IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems (BTAS 2009), pp. 1–6 (September 2009)Google Scholar
  2. 2.
    Proença, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The UBIRIS. v2: A database of visible wavelength images captured on-the move and at-a-distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(Rapid Posts) (2009)Google Scholar
  3. 3.
    Bharadwaj, S., Bhatt, H.S., Vatsa, M., Singh, R.: Periocular biometrics: When iris recognition fails. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), September 27-29, pp. 1–6 (2010)Google Scholar
  4. 4.
    Miller, P., Rawls, A., Pundlik, S., Woodard, D.: Personal identification using periocular skin texture. In: Proc. ACM 25th Symposium on Applied Computing (SAC 2010), pp. 1496–1500 (2010)Google Scholar
  5. 5.
    Woodard, D.L., Pundlik, S., Miller, P., Jillela, R., Ross, A.: On the fusion of periocular and iris biometrics in non-ideal imagery. In: Proc. Int. Conf. on Pattern Recognition (2010)Google Scholar
  6. 6.
    Adams, J., Woodard, D.L., Dozier, G., Miller, P., Bryant, K., Glenn, G.: Genetic-based type II feature extraction for periocular biometric recognition: Less is more. In: Proc. Int. Conf. on Pattern Recognition (2010)Google Scholar
  7. 7.
    Yu, H., Yang, J.: A Direct LDA Algorithm for High-Dimensional Data with Application to Face Recognition Interactive System Labs. Carnegie Mellon University, PittsburghGoogle Scholar
  8. 8.
    Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognition 33(10), 1713–1726 (2000)CrossRefGoogle Scholar
  9. 9.
    Heusch, G., Rodriguez, Y., Marcel, S.: Local binary patterns as an image preprocessing for face authentication. In: Proc. of International Conference on Automatic Face and Gesture Recognition, pp. 9–14 (2006)Google Scholar
  10. 10.
    Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. PAMI 18(8), 831–836 (1996)CrossRefGoogle Scholar
  11. 11.
    Liao, S., Fan, W., Chung, A., Yeung, D.: Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features. In: Proc. of the IEEE International Conference on Image Processing (ICIP), pp. 665–668 (2006)Google Scholar
  12. 12.
    Lyle, J.R., Miller, P.E., Pundlik, S.J., Woodard, D.L.: Soft biometric classification using periocular region features. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), September 27-29, pp. 1–7 (2010)Google Scholar
  13. 13.
    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
  14. 14.
    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 (PAMI) 24(7), 971–987 (2002)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Wang, X., Gong, H., Zhang, H., Li, B., Zhuang, Z.: Palmprint identification using boosting local binary pattern. In: Proc. 18th International Conference on Pattern Recognition, ICPR (2006)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Akanksha Joshi
    • 1
  • Abhishek Gangwar
    • 1
  • Renu Sharma
    • 1
  • Zia Saquib
    • 1
  1. 1.Center for Development of Advanced ComputingMumbaiIndia

Personalised recommendations