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Age Group and Gender Classification of Unconstrained Faces

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

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Abstract

Age and Gender classification of unconstrained imaging conditions has attracted an increased recognition as it is applicable in many real-world applications. Recent deep learning-based methods have shown encouraging performance in this field. We, therefore, propose an end-to-end deep learning-based method for robust age group and gender classification of unconstrained images. Particularly, we address the estimations problem with a classification based model that treats age value as a separate class and an independent label. The proposed deep convolutional neural network model learns the relevant informative age and gender representations directly from the image pixel. Technically, the model is first pre-trained on large-scale IMDb-WIKI facial aging dataset, and then fine-tuned on MORPH-II, another large-scale facial aging dataset to learn, and pick up the bias and particularities of each dataset. Finally, it is fine-tuned on the original dataset (OIU-Adience benchmark) with gender and age group labels. The experimental results when analyzed for estimation accuracy on OIU-Adience dataset, show that our model obtains the state-of-the-art performance in both age group and gender classification with an exact and one-off accuracy of 83.1% and 93.8% on age, and also an exact accuracy of 96.2% on gender.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Computer, University of KwaZulu-Natal, Durban, South Africa

    Olatunbosun Agbo-Ajala & Serestina Viriri

Authors
  1. Olatunbosun Agbo-Ajala
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  2. Serestina Viriri
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

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

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Agbo-Ajala, O., Viriri, S. (2019). Age Group and Gender Classification of Unconstrained Faces. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_32

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

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