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International Conference on Computer Analysis of Images and Patterns

CAIP 2021: Computer Analysis of Images and Patterns pp 285–296Cite as

Single View Facial Age Estimation Using Deep Learning with Cascaded Random Forests

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13053))

Abstract

The task of estimating a person’s real age using unconstrained facial images has been actively studied in biometrics research. We developed several deep learning architectures and supervision methods for facial age estimation and evaluate the impact of different pre-processing and face alignment (or normalization) methods on the feature embedding subspace. The proposed novel two-stage supervised learning model utilizes ResNeXt as a backbone combined with a two-layer random forest (TLRF) to estimate age. Our deep architectures are trained using a custom loss function to handle variations in gender, pose, illumination, ethnicity, expression and context, on the VGG-Face2 MIVIA Age Dataset with over 575K images, as part of the Guess the Age (GTA) contest. Surprisingly, face alignment using FANet during training did not improve accuracy. We were able to achieve an Age Accuracy and Regularity score \(AAR\,=\,7.02\) with a variance \(\sigma \,=\,1.16\) using only ResNeXt. The proposed ResNeXt+TLRF model improved age-class generalizability with a smaller variance of \(\sigma =0.98\) and a second best \(AAR\,=\,6.97\).

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Acknowledgments

Research partially supported by U.S. National Science Foundation award 2114141, Army Research Laboratory cooperative agreement W911NF1820285 and Army Research Office DURIP W911NF-1910181. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the U.S. Government or agency.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA

    Imad Eddine Toubal, Linquan Lyu, Dan Lin & K. Palaniappan

Authors
  1. Imad Eddine Toubal
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  2. Linquan Lyu
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  3. Dan Lin
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  4. K. Palaniappan
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Corresponding author

Correspondence to Imad Eddine Toubal .

Editor information

Editors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    Nicolas Tsapatsoulis

  2. University of Cyprus, Nicosia, Cyprus

    Andreas Panayides

  3. University of Cyprus, Nicosia, Cyprus

    Theo Theocharides

  4. Cyprus University of Technology, Limassol, Cyprus

    Andreas Lanitis

  5. University of Cyprus, Nicosia, Cyprus

    Constantinos Pattichis

  6. University of Salerno, Salerno, Italy

    Mario Vento

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Toubal, I.E., Lyu, L., Lin, D., Palaniappan, K. (2021). Single View Facial Age Estimation Using Deep Learning with Cascaded Random Forests. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_26

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  • DOI: https://doi.org/10.1007/978-3-030-89131-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89130-5

  • Online ISBN: 978-3-030-89131-2

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