Improving Driver Drowsiness Detection via Personalized EAR/MAR Thresholds and CNN-Based Classification

• URL: http://arxiv.org/abs/2604.22479v1
• Date: Fri, 24 Apr 2026 12:00:08 GMT
• Title: Improving Driver Drowsiness Detection via Personalized EAR/MAR Thresholds and CNN-Based Classification
• Abstract summary: Vision-based driver monitoring systems often rely on fixed Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) thresholds.<n>This paper proposes a personalized driver drowsiness detection system that monitors eyelid movements, head position, and yawning behavior in real time.<n>Deep learning-based Convolutional Neural Network (CNN) models are integrated to enhance accuracy in challenging scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driver drowsiness is a major cause of traffic accidents worldwide, posing a serious threat to public safety. Vision-based driver monitoring systems often rely on fixed Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) thresholds; however, such fixed values frequently fail to generalize across individuals due to variations in facial structure, illumination, and driving conditions. This paper proposes a personalized driver drowsiness detection system that monitors eyelid movements, head position, and yawning behavior in real time and provides warnings when signs of fatigue are detected. The system employs driver-specific EAR and MAR thresholds, calibrated before driving, to improve classical metric-based detection. In addition, deep learning-based Convolutional Neural Network (CNN) models are integrated to enhance accuracy in challenging scenarios. The system is evaluated using publicly available datasets as well as a custom dataset collected under diverse lighting conditions, head poses, and user characteristics. Experimental results show that personalized thresholding improves detection accuracy by 2-3% compared to fixed thresholds, while CNN-based classification achieves 99.1% accuracy for eye state detection and 98.8% for yawning detection, demonstrating the effectiveness of combining classical metrics with deep learning for robust real-time driver monitoring.

Related papers

• Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety [0.0]
  Driver drowsiness is estimated to contribute to 10%-20% of road accidents in Europe.<n>This paper proposes a deep reinforcement learning-based autonomous braking system that integrates vehicle dynamics with driver physiological data.<n>The proposed agent achieves a 99.99% success rate in avoiding collisions under both drowsy and non-drowsy conditions.
  arXiv  Detail & Related papers  (2026-04-15T13:39:56Z)
• Calibration-Free EEG-based Driver Drowsiness Detection with Online Test-Time Adaptation [43.83587667787693]
  Drowsy driving is a growing cause of traffic accidents, prompting recent exploration of electroencephalography (EEG)-based drowsiness detection systems.<n>In particular, the inter-subject variability of EEG signals leads to a domain shift problem, which makes it challenging to generalize drowsiness detection models to unseen target subjects.<n>We propose a novel driver drowsiness detection framework that leverages online test-time adaptation methods to dynamically adjust to target subject distributions.
  arXiv  Detail & Related papers  (2025-11-27T02:27:10Z)
• Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection [50.343419243749054]
  Anomaly detection is critical in fields such as medical diagnostics and industrial defect detection.<n> CLIP's coarse-grained image-text alignment limits localization and detection performance for fine-grained anomalies.<n>Crane improves the state-of-the-art ZSAD from 2% to 28%, at both image and pixel levels, while remaining competitive in inference speed.
  arXiv  Detail & Related papers  (2025-04-15T10:42:25Z)
• SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model [52.47816604709358]
  Video anomaly detection (VAD) aims to identify unexpected events in videos and has wide applications in safety-critical domains.<n> vision-language models (VLMs) have demonstrated strong multimodal reasoning capabilities, offering new opportunities for anomaly detection.<n>We propose SlowFastVAD, a hybrid framework that integrates a fast anomaly detector with a slow anomaly detector.
  arXiv  Detail & Related papers  (2025-04-14T15:30:03Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Evaluating ML Robustness in GNSS Interference Classification, Characterization & Localization [42.14439854721613]
  Jamming devices disrupt signals from the global navigation satellite system (GNSS)<n>This paper introduces an extensive dataset comprising snapshots obtained from a low-frequency antenna.<n>Our objective is to assess the resilience of machine learning (ML) models against environmental changes.
  arXiv  Detail & Related papers  (2024-09-23T15:20:33Z)
• Real-Time Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Detection [0.0]
  This study presents a real-time system designed to detect drowsiness using the Eye Aspect Ratio (EAR) and facial landmark detection techniques. By establishing a threshold for the EAR, the system identifies when eyes are closed, indicating potential drowsiness. Experiments show that the system reliably detects drowsiness with high accuracy while maintaining low computational demands.
  arXiv  Detail & Related papers  (2024-08-11T17:34:24Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders [0.0]
  An LSTM autoencoder-based architecture is utilized for drowsiness detection with ResNet-34 as feature extractor. The proposed model achieves detection rate of 0.8740 area under curve (AUC) and is able to provide significant improvements on certain scenarios.
  arXiv  Detail & Related papers  (2022-09-12T14:25:07Z)
• Driver Drowsiness Classification Based on Eye Blink and Head Movement Features Using the k-NN Algorithm [8.356765961526955]
  This work is to extend the driver drowsiness detection in vehicles using signals of a driver monitoring camera. For this purpose, 35 features related to the driver's eye blinking behavior and head movements are extracted in driving simulator experiments. A concluding analysis of the best performing feature sets yields valuable insights about the influence of drowsiness on the driver's blink behavior and head movements.
  arXiv  Detail & Related papers  (2020-09-28T12:37:38Z)
• Driver Safety Development Real Time Driver Drowsiness Detection System Based on Convolutional Neural Network [1.7188280334580195]
  This paper focuses on the challenge of driver safety on the road and presents a novel system for drowsiness detection. To detect the falling sleep state of the driver as the sign of drowsiness, Convolutional Neural Networks (CNN) are used.
  arXiv  Detail & Related papers  (2020-01-15T05:38:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.