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ROI-guided Daugman pupil localisation method

• 1、School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210000,China
• 2、School of Electronic Information Engineering, Wuxi University, Wuxi, Jiangsu 214000, China

Abstract：n order to solve the defects of the pupil centre localisation algorithm, which is susceptible to poor robustness and long time-consumption under light, occlusion and individual differences, a lightweight pupil localisation algorithm combining ROI detection and Daugman optimisation is proposed. The YOLOv8n model with GE_ShV2 as the backbone network is constructed to reduce 46.91% of the computation volume by structural optimization, and the model volume and parameters are reduced by 43.55% and 46.18% respectively, while maintaining 99% of the detection accuracy; the C2f_ESCA module is introduced into the neck network to achieve the sparse attention weighting of the channels, and the new candidate points are added on the basis of DySample. Importance-weighted sampling is added on the basis of DySample, and the sampling probability is dynamically allocated according to the noise reliability and edge gradient, which effectively enhances edge perception and reduces leakage. The Daugman algorithm introduces the grey level difference sum of squares calculation method and integral graph acceleration algorithm, which reduces the complexity from O(N) to O(1), effectively improves the positioning efficiency and anti-interference, and shortens the positioning time. The experimental results show that the improved algorithm performs more robustly when dealing with edge details and light changes.

Keywords： pupil detection YOLOv8 lightweighting daugman pupil localisation