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Paper FR-LM-T36.6

Rahman, Anima (The University of Warwick), Woodman, Roger (The University of Warwick), Donzella, Valentina (Queen Mary University of London)

Advancing Blink Detection in Driver Monitoring with Improved Eye Landmark Analysis

Scheduled for presentation during the Regular Session "S36a-Behavior Modeling and Decision-Making in Traffic Systems" (FR-LM-T36), Friday, November 21, 2025, 12:10−12:30, Surfers Paradise 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Driver Behavior Monitoring and Feedback Systems for Semi-autonomous Vehicles

Abstract

Accurate facial landmark detection is fundamental to many video-based driver monitoring applications. Identifying specific facial points such as the eyes, nose, and mouth is crucial for tracking facial movements and assessing the driver’s state. While widely used models like MediaPipe, Dlib, and FAN perform well on static images, they often struggle with video data, where consistency across frames and robustness to motion blur and head pose variation are essential. In this work, we address these challenges with EyeTrackNet, a two-stage spatiotemporal neural network designed to improve eye landmark prediction stability and blink detection in video sequences. Our method employs a convolutional LSTM to refine predictions over time by learning residual temporal corrections on top of features from a pre-trained spatial backbone. We evaluate EyeTrackNet on multiple video datasets and demonstrate it outperforms common baselines in landmark localisation and blink detection. Notably, EyeTrackNet maintains over 90% of frames below a Normalised Mean Error of 0.4, a key threshold for reliable blink detection, and achieves the highest overall F1 score on blink detection benchmarks.

 

 

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