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Precise eye localization using HOG descriptors

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Abstract

In this paper, we present a novel algorithm for precise eye detection. First, a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations. Then, a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of preselected eyes. Finally, we compare the eye detection results with three state-of-the-art works and a commercial software. The results show that our algorithm achieves the highest accuracy on the FERET and FRGCv1 databases, which is the most complete comparative presented so far.

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Authors and Affiliations

  1. I-TEAM, Universidad Politécnica de Valencia, Valencia, Spain

    David Monzo, Alberto Albiol, Jorge Sastre & Antonio Albiol

Authors
  1. David Monzo
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  2. Alberto Albiol
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  3. Jorge Sastre
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  4. Antonio Albiol
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Corresponding author

Correspondence to Alberto Albiol.

Additional information

This work has been partially supported by the grant TEC2009-09146 of the Spanish Government.

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Monzo, D., Albiol, A., Sastre, J. et al. Precise eye localization using HOG descriptors. Machine Vision and Applications 22, 471–480 (2011). https://doi.org/10.1007/s00138-010-0273-0

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  • DOI: https://doi.org/10.1007/s00138-010-0273-0

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