Gaze estimation based on swin transformer

Jiahui Chen; Jiaxin Ma; Xiwen Wang; Longzhao Huang; Yujie Li

doi:10.1117/12.3004623

16 August 2023 Gaze estimation based on swin transformer

Jiahui Chen, Jiaxin Ma, Xiwen Wang, Longzhao Huang, Yujie Li

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870R (2023) https://doi.org/10.1117/12.3004623
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

The direction of human eye gaze is an important human behavior information that reflects the level of attention and cognitive state of the gazer towards various visual information in the environment. Eye gaze estimation has wide application value in multiple fields such as medical care, market research, and human-computer interaction. In recent years, some studies have introduced Transformer into the task of eye gaze estimation and achieved advanced performance. Although Transformer has better global modeling ability, its structural characteristics are not suitable for multi-scale feature learning in visual tasks. In addition, the global self-attention calculation for images has high complexity. This paper introduces Swin Transformer into the field of eye gaze estimation, using self-attention mechanism to perform more flexible and effective global modeling of images. The self-attention calculation uses Windows Multi-head Self-Attention(W-MSA) and Shifted Windows Multi-head Self-Attention (SW-MSA), which greatly reduces the calculation of image self-attention. The experimental results demonstrate that the Swin Transformer can obtain good results in the task of eye gaze estimation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiahui Chen, Jiaxin Ma, Xiwen Wang, Longzhao Huang, and Yujie Li "Gaze estimation based on swin transformer", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870R (16 August 2023); https://doi.org/10.1117/12.3004623

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KEYWORDS

Transformers

Education and training

Modeling

Windows

Eye models

Eye

Visualization

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