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LAE : Long-Tailed Age Estimation

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Computer Analysis of Images and Patterns (CAIP 2021)

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Abstract

Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. Moreover, long-tailed recognition has been an important topic in facial age datasets, where the samples often lack on the elderly and children. To train a balanced age estimator, we propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification. The effectiveness of our approach has been demonstrated on the dataset provided by organizers of Guess The Age Contest 2021.

Z. Bao and Z. Tan—Co-First Author.

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Acknowledgements

This work was supported by the Chinese National Natural Science Foundation Projects #61961160704, #61876179, the External cooperation key project of Chinese Academy Sciences # 173211KYSB20200002, the Key Project of the General Logistics Department Grant No. AWS17J001, Science and Technology Development Fund of Macau (No. 0010/2019/AFJ, 0008/2019/A1 0025/2019/A-KP0019/2018/ASC).

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Correspondence to Jun Wan .

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Bao, Z. et al. (2021). LAE : Long-Tailed Age Estimation. 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_28

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  • DOI: https://doi.org/10.1007/978-3-030-89131-2_28

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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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