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Glasses detection on real images based on robust alignment

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Abstract

Automatic glasses detection on real face images is a challenging problem due to different appearance variations. Nevertheless, glasses detection on face images has not been thoroughly investigated. In this paper, an innovative algorithm for automatic glasses detection based on Robust Local Binary Pattern and robust alignment is proposed. Firstly, images are preprocessed and normalized in order to deal with scale and rotation. Secondly, eye glasses region is detected considering that the nosepiece of the glasses is usually placed at the same level as the center of the eyes in both height and width. Thirdly, Robust Local Binary Pattern is built to describe the eyes region, and finally, support vector machine is used to classify the LBP features. This algorithm can be applied as the first step of a glasses removal algorithm due to its robustness and speed. The proposed algorithm has been tested over the Labeled Faces in the Wild database showing a 98.65 % recognition rate. Influences of the resolution, the alignment of the normalized images and the number of divisions in the LBP operator are also investigated.

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Acknowledgments

Authors are grateful to anonymous reviewers for constructive feedback and insightful suggestions that greatly improved this article.

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Authors and Affiliations

  1. Fundación CTIC (Technological Center), Technological Scientific Park of Gijón, 33204, Gijón, Asturias, Spain

    Alberto Fernández & Rodrigo García

  2. Department of Computer Science and Engineering, University of Oviedo, Campus de Viesques, 33204, Gijón, Asturias, Spain

    Rubén Usamentiaga

  3. Treelogic, Technological Scientific Park of Asturias, 33428, Llanera, Asturias, Spain

    Rubén Casado

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  1. Alberto Fernández
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  2. Rodrigo García
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  3. Rubén Usamentiaga
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Correspondence to Alberto Fernández.

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Fernández, A., García, R., Usamentiaga, R. et al. Glasses detection on real images based on robust alignment. Machine Vision and Applications 26, 519–531 (2015). https://doi.org/10.1007/s00138-015-0674-1

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  • DOI: https://doi.org/10.1007/s00138-015-0674-1

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