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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 769–778Cite as

Age Classification from Facial Images: Is Frontalization Necessary?

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

Abstract

In the majority of the methods proposed for age classification from facial images, the preprocessing steps consist of alignment and illumination correction followed by the extraction of features, which are forwarded to a classifier to estimate the age group of the person in the image. In this work, we argue that face frontalization, which is the correction of the pitch, yaw, and roll angles of the headpose in the 3D space, should be an integral part of any such algorithm as it unveils more discriminative features. Specifically, we propose a method for age classification which integrates a frontalization algorithm before feature extraction. Numerical experiments on the widely used FGnet Aging Database confirmed the importance of face frontalization achieving an average increment in accuracy of 4.43%.

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Acknowledgments

This work has been funded in part by the Mexican National Council for Science and Technology (CONACYT) scholarship 328083 and by the UH Hugh Roy and Lillie Cranz Cullen Endowment Fund. The authors acknowledge the use of the Maxwell/Opuntia Cluster and the support of the Center of Advanced Computing and Data Systems at the University of Houston to carry out the research presented herein. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the sponsors.

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Authors and Affiliations

  1. Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, Texas, USA

    A. Báez-Suárez, C. Nikou & I. A. Kakadiaris

  2. Department of Computer Science, ITESM Campus Monterrey, Monterrey, Mexico

    A. Báez-Suárez & J. A. Nolazco-Flores

Authors
  1. A. Báez-Suárez
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  2. C. Nikou
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  3. J. A. Nolazco-Flores
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  4. I. A. Kakadiaris
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Corresponding authors

Correspondence to A. Báez-Suárez , C. Nikou , J. A. Nolazco-Flores or I. A. Kakadiaris .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Báez-Suárez, A., Nikou, C., Nolazco-Flores, J.A., Kakadiaris, I.A. (2016). Age Classification from Facial Images: Is Frontalization Necessary?. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_69

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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