@inproceedings{xie-fan-2025-learning,
    title = "A Learning-based Multi-Frame Visual Feature Framework for Real-Time Driver Fatigue Detection",
    author = "Xie, Liang  and
      Fan, Songlin",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-demo.7/",
    pages = "61--69",
    ISBN = "979-8-89176-191-9",
    abstract = "Driver fatigue is a significant factor contributing to road accidents, highlighting the need for reliable and accurate detection methods. In this study, we introduce a novel learning-based multi-frame visual feature framework (LMVFF) designed for precise fatigue detection. Our methodology comprises several clear and interpretable steps. Initially, facial landmarks are detected, enabling the calculation of distances between eyes, lips, and the assessment of head rotation angles based on 68 identified landmarks. Subsequently, visual features from the eye region are extracted, and an effective visual model is developed to accurately classify eye openness. Additionally, features characterizing lip movements are analyzed to detect yawning, thereby enriching fatigue detection through continuous monitoring of eye blink frequency, yawning occurrences, and head movements. Compared to conventional single-feature detection approaches, LMVFF significantly reduces instances of fatigue misidentification. Moreover, we employ various quantization and compression techniques for multiple computation stages, substantially reducing the latency of our system and achieving a real-time frame rate of 25-30 FPS for practical applications."
}