Robust eye centre extraction using the Hough Transform

  • David E. Benn
  • Mark S. Nixon
  • John N. Carter
Facial Features Localisation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1206)


Finding the eyes is an important stage of feature extraction in automatic face recognition. Current approaches include standard feature extraction techniques using heuristic methods specifically developed for human eyes. We present a new method for finding eye centres using a gradient decomposed Hough Transform (HT) which embodies the natural concentricity of the eye region in a peak reinforcement scheme to improve accuracy and robustness. This enhances a standard feature extraction technique with an analytic approach, which can be applied to the whole face without priming of estimates of eye position and size. In a database of 54 eyes this new method is shown to be less constrained, more robust and resulted in a three-fold improvement in accuracy over using the standard HT.


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  1. [1]
    A. S. Aguado, M. E. Montiel and M. S. Nixon, “On Using Directional Information for Parameter Space Decomposition in Ellipse Detection”, Patt. Recog., 29(3) pp. 369–381, (1996).Google Scholar
  2. [2]
    R. Chellappa, C. Wilson and S. Sirohey, “Human and Machine Recognition of Faces: A survey”. Proc of the IEEE 83 (5) pp. 704–741, (1995).Google Scholar
  3. [3]
    S. R. Gunn and M. S. Nixon, “Snake Head Boundary Extraction Using Global and Local Energy Minimisation”, Proc. 13 ICPR, 2, pp. 581–585, (1996).Google Scholar
  4. [4]
    X. Jia and M. S. Nixon, “Extending the Feature Vector for Automatic Face Recognition”. IEEE Trans. PAMI., 17 (12) pp. 1167–1176, (1995).Google Scholar
  5. [5]
    R. Kothari and J. L. Mitchell, Detection of Eye Locations in Unconstrained Visual Images”, Proc. ICIP '96, III, pp. 519–523, (1996).Google Scholar
  6. [6]
    M. Nixon, “Eye Spacing Measurement for Facial Recognition”. SPIE Proc, 575, pp. 279–285, (1985).Google Scholar
  7. [7]
    D. Riesfield, H. Wolfson and Y. Yeshuran, “Context-free Attentional Operators: The Generalised Symmetry Transform”. Int. J. of Comp. Vision 14 pp. 119–130, (1995).Google Scholar
  8. [8]
    D. Riesfield and Y. Yeshurun, “Robust Detection of Facial Features by Generalised Symmetry”. Proc. 11th Int Conf. on Patt. Recog., pp. 117–120, (1992).Google Scholar
  9. [9]
    L. Stringa, “Eyes Detection For Face Recognition”. Applied Artificial Intelligence 7 pp. 365–382, (1993).Google Scholar
  10. [10]
    X. Xie, R. Sudhakar and H. Zhuang, “On Improving Eye Feature Extraction using Deformable Templates”. Patt. Recog., 27(6) pp. 791–799, (1994).Google Scholar
  11. [11]
    X. Xie, R. Sudhakar and H. Zhuang, “Real-Time Eye Feature Tracking from a Video Sequence Using Kaiman Filter”. IEEE Trans. on SMC, 25 (12) pp. 1568–577, (1995).Google Scholar
  12. [12]
    A. Yuille, D. Cohen and P. Hallinan, “Feature Extraction from Faces using Deformable Templates”. Int. J. Comp. Vision 8(20) pp. 99–111, (1989Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • David E. Benn
    • 1
  • Mark S. Nixon
    • 1
  • John N. Carter
    • 1
  1. 1.Department of Electronics and Computer ScienceUniversity of SouthamptonHighfieldEngland

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