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An integrated neural network model for pupil detection and tracking.

Shi, L., Wang, C., Tian, F. and Jia, H., 2021. An integrated neural network model for pupil detection and tracking. Soft Computing, 25, 10117-10127.

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LVCF_prepublish.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1007/s00500-021-05984-y


The accurate detection and tracking of pupil is important to many applications such as human computer interaction, driver’s fatigue detection and diagnosis of brain diseases. Existing approaches however face challenges in handing low quality of pupil images. In this paper, we propose an integrated pupil tracking framework namely LVCF, based on deep learning. LVCF consists of the pupil detection model VCF which is an end-to-end network, and the LSTM pupil motion prediction model which applies LSTM to track pupil’s position. The proposed network was trained and evaluated on 10600 images and 75 videos taken from 3 realistic datasets. Within an error threshold of 5 pixels, VCF achieves an accuracy of more than 81%, and LVCF outperforms the state of arts by 9% in terms of percentage of pupils tracked. The project of LCVF is available at

Item Type:Article
Uncontrolled Keywords:Eye-tracking ; Pupil detection ; Deep learning ; Convolutional neural networks ; Long short-term memory
Group:Faculty of Science & Technology
ID Code:35772
Deposited By: Symplectic RT2
Deposited On:14 Jul 2021 16:02
Last Modified:02 Jul 2022 01:08


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