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Guess the Age 2021: Age Estimation from Facial Images with Deep Convolutional Neural Networks

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Computer Analysis of Images and Patterns (CAIP 2021)

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

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Abstract

Guess The Age 2021 is an international contest meant for teams able to propose methods based on modern Deep Convolutional Neural Networks (DCNNs) for age estimation from facial images. In order to allow the teams to train effective models, the Mivia Age Dataset, including 575.073 images annotated with age labels, was provided as training set; it is among the biggest publicly available datasets of faces in the world with age annotations. The performance of the methods submitted by the teams have been evaluated on a test set of more than 150.000 images, different from the ones available in the training set; a new index, called AAR, which takes into account the age estimation accuracy, namely the average error, and the regularity, i.e. the standard deviation of the error, has been adopted for drawing up the final ranking. The BTWG team, winner of the contest, achieved an impressive AAR equal to 7.94, with a novel method demonstrating an impressive accuracy and regularity in facial age estimation.

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Notes

  1. 1.

    http://gta2021.unisa.it/.

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Authors and Affiliations

  1. Department of Computer Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, Italy

    Antonio Greco

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  1. Antonio Greco
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Correspondence to Antonio Greco .

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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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Greco, A. (2021). Guess the Age 2021: Age Estimation from Facial Images with Deep Convolutional Neural Networks. 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_24

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  • DOI: https://doi.org/10.1007/978-3-030-89131-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89130-5

  • Online ISBN: 978-3-030-89131-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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