A Robust Method for Near Infrared Face Recognition Based on Extended Local Binary Pattern

  • Di Huang
  • Yunhong Wang
  • Yiding Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)


Face recognition is one of the most successful applications in biometric authentication. However, methods reported in the literature still suffer from some problems which prevent the further development in face recognition. This paper presents a novel robust method for face recognition under near infrared (NIR) lighting condition based on Extended Local Binary Pattern (ELBP), which solves the problems produced by variations of illumination rightly, since the NIR images are insensitive to variations of ambient lighting, and ELBP can extract adequate texture features form the NIR images. By combining the local feature vectors, a global feature vector is formed and as the global feature vectors extracted by ELBP operator often have very high dimensions, a classifier has been trained using the AdaBoost algorithm to select the most representative features for better performance and dimensionality reduction. Compared with the huge number of features produced by ELBP operator, only a small part of the features are selected in this paper, which saves much computation and time cost. The comparison with the results of classic algorithms proves the effectiveness of the proposed method.


Face recognition Near Infrared (NIR) Extended Local Binary Pattern (ELBP) local feature vector global feature vector AdaBoost 


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  1. 1.
    Zhao, W.Y., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003)Google Scholar
  2. 2.
    Dowdall, J., Pavlidis, I., Bebis, G.: Face Detection in the Near-IR Spectrum. Image and Vision Computing 21, 565–578 (2003)CrossRefGoogle Scholar
  3. 3.
    Pan, Z.H., Healey, G., Prasad, M., Tromberg, B.: Face Recognition in Hyperspectral Images. IEEE Trans. Pattern Analysis and Machine Intelligence 25, 1552–1560 (2003)CrossRefGoogle Scholar
  4. 4.
    Li, S.Z., Chu, L.F., Liao, S.C., Zhang, L.: Illumination Invariant Face Recognition Using Near-Infrared Images. IEEE Trans. Pattern Analysis and Machine Intelligence 29, 627–639 (2007)CrossRefGoogle Scholar
  5. 5.
    Timo, A., Abdenour, H., Matti, P.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Huang, Y.G., Wang, Y.H., Tan, T.N.: Combining Statistics of Geometrical and Correlative Features for 3D Face Recognition. British Machine Vision Association 1, 391–395 (2006)Google Scholar
  7. 7.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefGoogle Scholar
  8. 8.
    Moghaddam, B., Pentland, A.: Beyond Eigenface: Probabilistic Matching for Face Recognition. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 30–35 (1998)Google Scholar
  9. 9.
    Viola, P., Jones, M.: Rapid Object Detection Using a boosted cascade of simple features. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (2001)Google Scholar
  10. 10.
    Phillips, P.J., Moon, H., Rauss, P., Rizvi, S.A.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22, 1090–1104 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Di Huang
    • 1
  • Yunhong Wang
    • 1
  • Yiding Wang
    • 2
  1. 1.School of Computer Science and Engineering, Beihang University, Beijing, 100083China
  2. 2.Graduate University of Chinese Academy of Science, Beijing, 100049China

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