Pupil size detection based on convolution neural network applied on portable pupillometer

Xin Zhang; Yi-Yung Chen Sr.; Allen Jong-Woei Whang Sr.; Chih-Hsien Tsai Jr.; Wei-Chieh Tseng

doi:10.1117/12.2593861

1 August 2021 Pupil size detection based on convolution neural network applied on portable pupillometer

Xin Zhang, Yi-Yung Chen Sr., Allen Jong-Woei Whang Sr., Chih-Hsien Tsai Jr., Wei-Chieh Tseng

Proceedings Volume 11843, Applications of Machine Learning 2021; 118430Y (2021) https://doi.org/10.1117/12.2593861
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Conference Poster

Abstract

The pupil response to light is usually used for inspection of a patient's activity. When medical personnel performs light reflection examinations, it depends on the experience of medical staff. Even for the same person, they may induce variant measurement results in different situations. This paper proposes an algorithm that can calculate pupil size based on Convolution Neural Network (CNN). The study aims to measure pupil size on a mobile device convenient for medical staff to measure real-time. Still, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses for the best fitting. Therefore, we use the major and minor axis of an ellipse to represent the size of pupils and use the two parameters as the output of the network. The compares the mean error with changing the depth of the network and the field of view (FOV) of the convolution filter. Finally, the mean error of the pupil length is 7.63%. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. In the operation speed, we use mobile device systems at 36 frames per second to use mobile device systems for pupil size prediction.

Citation Download Citation

Xin Zhang, Yi-Yung Chen Sr., Allen Jong-Woei Whang Sr., Chih-Hsien Tsai Jr., and Wei-Chieh Tseng "Pupil size detection based on convolution neural network applied on portable pupillometer", Proc. SPIE 11843, Applications of Machine Learning 2021, 118430Y (1 August 2021); https://doi.org/10.1117/12.2593861

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