A pupil detection method based on Unet with attention module and shape-prior loss

Wenhui Song; Hui Wang; Yawei Gui; Ruochen Dang; Bingliang Hu; Quan Wang

doi:10.1117/12.2651952

20 January 2023 A pupil detection method based on Unet with attention module and shape-prior loss

Wenhui Song, Hui Wang, Yawei Gui, Ruochen Dang, Bingliang Hu, Quan Wang

Proceedings Volume 12558, AOPC 2022: Optical Spectroscopy and Imaging; 125580L (2023) https://doi.org/10.1117/12.2651952
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

In recent years, pupil detection in human eye images or videos has played a key role in many fields. In the field of eye tracking, the position of the center of the pupil is a basic problem, and the error of pupil detection will be magnified in subsequent calculations, which will seriously affect the performance of eye tracking. In this paper, we propose to use the currently popular semantic segmentation network for pupil detection task. We first train the Unet architecture as a benchmark, then introduce two different attention modules into Unet, and compare with the benchmark network. The results show that our method has a higher detection rate within 1-15 pixel errors. We also added an ellipse fitting error term to the loss function of the network to further improve the network performance. The training of the model is done on the LPW dataset. Finally, we also investigate the effect of data augmentation on generalization performance, with the model trained on the LPW dataset and tested on the I-SOCIAL_DB dataset. Although data enhancement slightly reduces the detection rate of the model in the original data set, it can improve the generalization performance of the model.

Citation Download Citation

Wenhui Song, Hui Wang, Yawei Gui, Ruochen Dang, Bingliang Hu, and Quan Wang "A pupil detection method based on Unet with attention module and shape-prior loss", Proc. SPIE 12558, AOPC 2022: Optical Spectroscopy and Imaging, 125580L (20 January 2023); https://doi.org/10.1117/12.2651952

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