Multi-Feature Long Short-Term Memory Facial Recognition for Real-Time Automated Drowsiness Observation of Automobile Drivers with Raspberry Pi 4
We developed a multi-feature drowsiness detection model employing eye aspect ratio (EAR), mouth aspect ratio (MAR), head pose angles (yaw, pitch, and roll), and a Raspberry Pi 4 for real-time applications. The model was trained on the NTHU-DDD dataset and optimized using long short-term memory (LSTM...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/92/1/52 |
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