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Age Estimation by LS-SVM Regression on Facial Images

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Book cover Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

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Abstract

Determining the age of a person just by using an image of his/her face is a research topic in Computer Vision that is being extensively worked on. In contrast to say expression analysis, age determination is dependent on a number of factors. To construe the real age of a person is an esoteric task. The changes that appear on a face are not only due to aging, but also a number of factors like stress, appropriate rest etc. In this paper an approach has been developed to determine true age of a person by making use of some existing algorithms and combining them for maximum efficiency. The image is represented using an Active Appearance Model (AAM). The AAM uses geometrical ratio of the local face features along with wrinkle analysis. Next, to enhance the feature selection, Principle Component Analysis (PCA) is done. For the learning process a Support Vector Machine is used. Relationships in the image are obtained by making use of binarized statistical image features (BSIF) and the patterns are stored in Local Binary Pattern Histograms (LBPH). This histogram acts as input for the learning unit. The SVM is made to learn the patterns by studying the LBPH. Finally after the learning phase, when a new image is taken, a Least Square-Support Vector Machine Regression model (LS-SVM) is used to predict the final age of the person in the image.

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Authors and Affiliations

  1. IIT-Madras, Chennai, India

    Shreyank N. Gowda

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  1. Shreyank N. Gowda
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Correspondence to Shreyank N. Gowda .

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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Gowda, S.N. (2016). Age Estimation by LS-SVM Regression on Facial Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_36

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

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