{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T15:32:34Z","timestamp":1782228754980,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62027810"],"award-info":[{"award-number":["62027810"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Fatigue driving is one of the crucial factors causing traffic accidents. Most existing fatigue driving detection algorithms overlook individual driver characteristics, potentially leading to misjudgments. This article presents a novel detection algorithm that utilizes facial multi-feature fusion, thoroughly considering the driver\u2019s individual characteristics. To improve the judging accuracy of the driver\u2019s facial expressions, a personalized threshold is proposed based on the normalization of the driver\u2019s eyes and mouth opening and closing instead of the traditional average threshold, as individual drivers have different eye and mouth sizes. Given the dynamic changes in fatigue level, a sliding window model is designed for further calculating blinking duration ratio (BF), yawning frequency (YF), and nodding frequency (NF), and these evaluation indexes are used in the feature fusion model. The reliability of the algorithm is verified by the actual test results, which show that the detection accuracy reaches 95.6% and shows good application potential in fatigue detection applications. In this way, facial multi-feature fusion and fully considering the driver\u2019s individual characteristics makes fatigue driving detection more accurate.<\/jats:p>","DOI":"10.3390\/a18050247","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T08:02:57Z","timestamp":1745568177000},"page":"247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Multi-Feature Fusion Algorithm for Fatigue Driving Detection Considering Individual Driver Differences"],"prefix":"10.3390","volume":"18","author":[{"given":"Meng","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"China Institute of Marine Technology and Economy, Beijing 100081, China"},{"name":"National Key Laboratory of Human Factors Engineering, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengming","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61904","DOI":"10.1109\/ACCESS.2019.2914373","article-title":"A survey on state-of-the-art drowsiness detection techniques","volume":"7","author":"Ramzan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106358","DOI":"10.1016\/j.aap.2021.106358","article-title":"Alcohol-related traffic laws and drunk-driving fatal accidents","volume":"161","author":"Wright","year":"2021","journal-title":"Accid. 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