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Rotation-Invariant Facial Feature Detection Using Gabor Wavelet and Entropy

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Image Analysis and Recognition (ICIAR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3656))

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Abstract

A novel technique for facial feature detection in images of frontal faces is presented. We use a set of Gabor wavelet coefficients in different orientations and frequencies to analyze and describe facial features. However, due to the lack of sufficient local structures for describing facial features, Gabor wavelets can not perfectly capture the wide range of possible variations in the appearance of facial features, and thus can give many false positive (and sometimes false negative) responses. We show that the performance of such a feature detector can be significantly improved by using the local entropy of features. Complex regions in a face image, such as the eye, exhibit unpredictable local intensity and hence high entropy. Our method is robust against image rotation, varying brightness, varying contrast and a certain amount of scaling.

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Author information

Authors and Affiliations

  1. Department of System Design Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Ehsan Fazl Ersi & John S. Zelek

Authors
  1. Ehsan Fazl Ersi
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  2. John S. Zelek
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, P.O. Box, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2005 Springer-Verlag Berlin Heidelberg

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Ersi, E.F., Zelek, J.S. (2005). Rotation-Invariant Facial Feature Detection Using Gabor Wavelet and Entropy. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_126

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  • DOI: https://doi.org/10.1007/11559573_126

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29069-8

  • Online ISBN: 978-3-540-31938-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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