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Occlusion Contrasts for Self-Supervised Facial Age Estimation

Published: 20 October 2021 Publication History

Abstract

In this paper, we propose an Occlusion Contrast(OCCO) approach for self-supervised facial partial occluded age estimation. Unlike the conventional facial age estimation approaches which utilize fully-visible faces as input data that does not generalize well for occlusion images, our approach aims to ignore the occlusion and only focus on the non-occluded facial areas so that we can improve the occluded facial age estimation accuracy. To achieve this, we utilize self-supervised contrastive learning to learn non-occluded feature representation, since contrastive learning makes the distances between the anchor and positive samples as close as possible in embedded space, while simultaneously pushing apart the negative samples. Furthermore, our OCCO incorporates with ordinal relationship of different ages, which is modeled by the deep label distribution learning. Considering that face aging datasets usually undergo a label imbalance problem, we employ the cost-sensitive strategy to constrain the learning of classifier. Extensive experimental results on two face aging datasets show that our OCCO not only achieve satisfactory performance over the masked faces but also comparable to the state-of-the-art age estimation methods for raw facial images.

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Cited By

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  • (2023)Age Prediction From Face Images Via Contrastive Learning2023 18th International Conference on Machine Vision and Applications (MVA)10.23919/MVA57639.2023.10216074(1-6)Online publication date: 23-Jul-2023
  • (2023)Self-Supervised Learning for 3-D Point Clouds Based on a Masked Linear AutoencoderIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.333708861(1-11)Online publication date: 2023
  • (2022)Age Estimation of Occluded Faces using EfficientNet-B3 CNN ModelEICC 2022: Proccedings of the European Interdisciplinary Cybersecurity Conference10.1145/3528580.3532992(95-96)Online publication date: 21-Jul-2022

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  1. Occlusion Contrasts for Self-Supervised Facial Age Estimation

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    cover image ACM Conferences
    MULL'21: Multimedia Understanding with Less Labeling on Multimedia Understanding with Less Labeling
    October 2021
    64 pages
    ISBN:9781450386814
    DOI:10.1145/3476098
    • Program Chairs:
    • Xiu-Shen Wei,
    • Han-Jia Ye,
    • Jufeng Yang,
    • Jian Yang
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    Published: 20 October 2021

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    Author Tags

    1. contrastive learning
    2. facial age estimation
    3. label distribution learning

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    • the National Science Foundation of China
    • the West Light Talent Program of the Chinese Academy of Sciences
    • the Youth Science and Technology Talents Enrollment Projects of Ningxia

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    October 24, 2021
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    View all
    • (2023)Age Prediction From Face Images Via Contrastive Learning2023 18th International Conference on Machine Vision and Applications (MVA)10.23919/MVA57639.2023.10216074(1-6)Online publication date: 23-Jul-2023
    • (2023)Self-Supervised Learning for 3-D Point Clouds Based on a Masked Linear AutoencoderIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.333708861(1-11)Online publication date: 2023
    • (2022)Age Estimation of Occluded Faces using EfficientNet-B3 CNN ModelEICC 2022: Proccedings of the European Interdisciplinary Cybersecurity Conference10.1145/3528580.3532992(95-96)Online publication date: 21-Jul-2022

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