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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 119
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| Paper Title: | Real-Time Driver Drowsiness Detection using CNN-GRU Model with Facial Features and Behavioural Analysis |
| Authors Name: | Anju G , Ahana sumesh , Arza Fathima M , Chandana S Krishna , Fathimathu Riza P V |
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IJRTI_210390
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| Published Paper Id: | IJRTI2604028 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | Drowsiness during driving is a significant contributor to road accidents, especially during prolonged travel or under low visibility conditions. To address this, the proposed study presents a real-time drowsiness detection system based on a deep learning hybrid model combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The model analyses behavioural features such as eye aspect ratio (EAR), mouth aspect ratio (MAR), and head pose, extracted using facial landmark detection through MediaPipe. To ensure robustness in variable lighting conditions, Retinex theory is applied for image preprocessing, enhancing contrast and correcting illumination inconsistencies. This improves the reliability of visual feature extraction. The CNN component is responsible for learning spatial features from facial images, while the GRU captures temporal patterns across video frames. This allows the system to detect signs of fatigue such as prolonged eye closure, yawning, and head nodding over time. The hybrid design leverages both spatial and temporal cues for more accurate classification. The model is trained and evaluated on a labeled dataset and demonstrates strong performance across precision, recall, and accuracy metrics. By integrating temporal modeling and illumination correction, the system adapts effectively to real-world environments. This contributes to early and accurate detection of driver fatigue. Ultimately, the approach enhances road safety by providing timely alerts and reducing accident risk . |
| Keywords: | CNN, GRU, BLIP, Retinex Theory, OpenCV, Facial Landmark Extraction, ADHE, MediaPipe |
| Cite Article: | "Real-Time Driver Drowsiness Detection using CNN-GRU Model with Facial Features and Behavioural Analysis", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a193-a200, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604028.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604028
Registration ID:210390
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a193-a200 Country: Kannur, Kerala, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604028 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604028 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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