An Irregular Pupil Localization Network Driven by ResNet Architecture

Genjian Yang, Wenbai Chen (), Peiliang Wu, Jianping Gou and Xintong Meng
Additional contact information
Genjian Yang: School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
Wenbai Chen: School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
Peiliang Wu: School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
Jianping Gou: College of Computer and Information Science, College of Software, Southwest University, Chongqing 400715, China
Xintong Meng: School of Automation, Beijing Information Science and Technology University, Beijing 100192, China

Mathematics, 2024, vol. 12, issue 17, 1-14

Abstract: The precise and robust localization of pupils is crucial for advancing medical diagnostics and enhancing user experience. Currently, the predominant method for determining the center of the pupil relies on the principles of multi-view geometry, necessitating the simultaneous operation of multiple sensors at different angles. This study introduces a single-stage pupil localization network named ResDenseDilateNet, which is aimed at utilizing a single sensor for pupil localization and ensuring accuracy and stability across various application environments. Our network utilizes near-infrared (NIR) imaging to ensure high-quality image output, meeting the demands of most current applications. A unique technical highlight is the seamless integration of the efficient characteristics of the Deep Residual Network (ResNet) with the Dense Dilated Convolutions Merging Module (DDCM), which substantially enhances the network’s performance in precisely capturing pupil features, providing a deep and accurate understanding and extraction of pupil details. This innovative combination strategy greatly improves the system’s ability to handle the complexity and subtleties of pupil detection, as well as its adaptability to dynamic pupil changes and environmental factors. Furthermore, we have proposed an innovative loss function, the Contour Centering Loss, which is specifically designed for irregular or partially occluded pupil scenarios. This method innovatively calculates the pupil center point, significantly enhancing the accuracy of pupil localization and robustness of the model in dealing with varied pupil morphologies and partial occlusions. The technology presented in this study not only significantly improves the precision of pupil localization but also exhibits exceptional adaptability and robustness in dealing with complex scenarios, diverse pupil shapes, and occlusions, laying a solid foundation for the future development and application of pupil localization technology.

Keywords: pupil localization; center determination; ResNet; dense dilated convolutions; NIR (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/17/2703/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/17/2703/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:17:p:2703-:d:1467458

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().