Graph Matching Iris Image Blocks with Local Binary Pattern

  • Zhenan Sun
  • Tieniu Tan
  • Xianchao Qiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Iris-based personal identification has attracted much attention in recent years. Almost all the state-of-the-art iris recognition algorithms are based on statistical classifier and local image features, which are noise sensitive and hardly to deliver perfect recognition performance. In this paper, we propose a novel iris recognition method, using the histogram of local binary pattern for global iris texture representation and graph matching for structural classification. The objective of our idea is to complement the state-of-the-art methods with orthogonal features and classifier. In the texture-rich iris image database UPOL, our method achieves higher discriminability than state-of-the-art approaches. But our algorithm does not perform well in the CASIA database whose images are less textured. Then the value of our work is demonstrated by providing complementary information to the state-of-the-art iris recognition systems. After simple fusion with our method, the equal error rate of Daugman’s algorithm could be halved.


  1. 1.
    Daugman, J.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Trans. Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  2. 2.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Trans. Image Processing 13(6), 739–750 (2004)CrossRefGoogle Scholar
  3. 3.
    Sanchez-Avila, C., Sanchez-Reillo, R.: Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation. Pattern Recognition 38(2), 231–240 (2005)CrossRefGoogle Scholar
  4. 4.
    Sun, Z., Wang, Y., Tan, T., Cui, J.: Improving Iris Recognition Accuracy via Cascaded Classifiers. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews 35(3), 435–441 (2005)CrossRefGoogle Scholar
  5. 5.
    Mäenpää, T., Pietikäinen, M.: Texture analysis with local binary patterns. In: Chen, C., Wang, P. (eds.) Handbook of Pattern Recognition and Computer Vision, ch.1, pp. 197–216. World Scientific, Singapore (2005)CrossRefGoogle Scholar
  6. 6.
    Dobeš, M., Machala, L.: UPOL Iris Database,
  7. 7.
    CASIA Iris Image Database,

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zhenan Sun
    • 1
  • Tieniu Tan
    • 1
  • Xianchao Qiu
    • 1
  1. 1.Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingP.R. China

Personalised recommendations