This study presents a comprehensive bibliometric analysis of research trends in drowsiness detection using facial features, examining publications from 2014 to 2024. Employing a PRISMA-guided methodology, we extracted and analyzed 347 documents from the Scopus database, revealing significant patterns in research productivity, citation impact, geographical distribution, institutional contributions, and thematic evolution. Our analysis identifies an annual growth rate of 18.59% in publication volume, with a notable surge after 2019, coinciding with the widespread adoption of deep learning approaches. Geographically, Asian countries dominate research output, with India (182 publications) and China (100 publications) leading contributions, while China garnered the highest citation impact (1370 citations). Through sophisticated co-occurrence network analysis, we identified four distinct research clusters: (1) Physiological Insights via Neural Networks, (2) Computer Vision-Based Drowsiness Detection, (3) Multi-Modal Fatigue Detection for Accident Prevention, and (4) Deep Learning for Biomedical Signal Analysis. Temporal analysis of keyword evolution reveals a shift from traditional machine learning approaches toward deep neural networks, Internet of Things integration, and real-time monitoring systems. Our thematic mapping further categorizes research into basic themes (CNNs, eye aspect ratio), motor themes (driver fatigue detection, cloud computing), niche themes (3D head pose estimation, behavioral measurement), and emerging/declining themes (pupil detection, blink detection systems).nbsp; Systematic analysis of deployment metrics reveals critical gaps: only 3.3% of top-cited papers report frames per second (FPS), and none report latency or time-to-alarm (TTA), despite frequent claims of real-time capability. Analysis of multimodal approaches shows 74% of studies focus exclusively on facial features, while 26% incorporate physiological or vehicular signals. This comprehensive bibliometric landscape illuminates the field's evolution, identifies research gaps particularly regarding deployment readiness, ethnicity-specific considerations and low-light environments, and provides a strategic roadmap for future research directions in drowsiness detection systems.
Subowo et al. (Thu,) studied this question.