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Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

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Abstract

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.

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Notes

  1. www.imdb.com.

  2. https://en.wikipedia.org/.

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Acknowledgments

This work was supported by the KTI-SUPSI (#2-69650-14) project and by an NVidia GPU grant.

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Authors and Affiliations

  1. Computer Vision Lab, D-ITET, ETH Zurich, Switzerland

    Rasmus Rothe, Radu Timofte & Luc Van Gool

  2. VISICS/iMinds, ESAT, KU Leuven, Belgium

    Luc Van Gool

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  1. Rasmus Rothe
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  2. Radu Timofte
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  3. Luc Van Gool
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Correspondence to Rasmus Rothe.

Additional information

Communicated by Cordelia Schmid, Sergio Escalera, Jordi Gonzàlez, Xavier Barò, Isabelle Guyon and Hugo Jair Escalante.

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Rothe, R., Timofte, R. & Van Gool, L. Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks. Int J Comput Vis 126, 144–157 (2018). https://doi.org/10.1007/s11263-016-0940-3

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