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Young-gaze: an appearance-based gaze estimation solution for adolescents

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Abstract

According to the World Health Organization survey, the global incidence of adolescent mental illness is 28%, while the disease detection rate is only 24.6%. Many existing works use complex eye-tracking devices to study adolescent autism, depression, and other mental illness. In this paper, we propose a gaze estimation method to replace eye-tracking devices. Appearance-based methods with deep learning can predict the point of gaze by using a monocular camera, which requires a large number of samples to learn. However, the samples collected in publicly available gaze estimation datasets are mainly adults and not adolescents. To address the above issue, our work makes two contributions. First, we collected images from 107 adolescents aged 10–14 years by laptops under uncontrolled conditions to create the Young-Gaze dataset. Second, we propose a Multi-scale Feature Fusion-based Calibration Network (MFFC-Net) to deeply fuse the eye-face features for gaze estimation. The proposed MFFC-Net achieves the better performance on Young-Gaze and other public datasets.

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Data and materials availability

We proposed Young-Gaze dataset in this paper. The dataset is supported by Shanghai Zhangjiang Group Middle School

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Acknowledgements

This work was supported in part by Wenzhou Major Scientific and Technological Innovation Project under Grant ZY2023003, in part by Medical Health Science and Technology Project of Zhejiang Provincial Health Commission under Grant 2022PY091, and in part by ShangDa Translation Medicine Funding supported by Wenzhou Institute of Shanghai University under Grant SDTMF2022KP06.

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Authors

Contributions

Xiaofeng Lu, Zichen Zhao and Weitao Ke wrote the main manuscript text, and Qingsong Yan prepared all figures and tables. Zhi Liu reviewed and revised the manuscript.

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Correspondence to Zhi Liu.

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We have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Appendices

Appendix

Network details

See Tables

Table 5 Description of Conv Block-1, Conv Block-2 and Conv Block-3 in the facial feature excraction network. Conv2d represents convolutional layers (k: filter size, s: stride, p: padding)

5,

Table 6 Description of Conv Block-4, Conv Block-5 and Conv Block-6 in the calibration network. Conv2d represents convolutional layers (k: filter size, s: stride, p: padding)

6,

Table 7 Description of fully connected layers in the facial feature excraction network

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Table 8 Description of fully connected layers in the calibration network

8

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Lu, X., Zhao, Z., Ke, W. et al. Young-gaze: an appearance-based gaze estimation solution for adolescents. SIViP 18, 7145–7155 (2024). https://doi.org/10.1007/s11760-024-03381-0

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