# Driver Drowsiness Detection Using Ensemble Convolutional Neural Networks on YawDD

arXiv: https://arxiv.org/abs/2112.10298 · HTML: https://mlsanity.com/paper/2112.10298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection](https://mlsanity.com/paper/1904.07312/) (2019)
- [Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review](https://mlsanity.com/paper/2112.06459/) (2021)
- [Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection](https://mlsanity.com/paper/1801.02325/) (2018)
- [An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimation](https://mlsanity.com/paper/1802.02137/) (2018)

## Built on by (most cited first, 1 of 1)

- [Federated Learning for Drowsiness Detection in Connected Vehicles](https://mlsanity.com/paper/2405.03311/) (2024)

## Abstract (arXiv)

Driver drowsiness detection using videos/images is one of the most essential areas in today's time for driver safety. The development of deep learning techniques, notably Convolutional Neural Networks (CNN), applied in computer vision applications such as drowsiness detection, has shown promising results due to the tremendous increase in technology in the recent few decades. Eyes that are closed or blinking excessively, yawning, nodding, and occlusion are all key aspects of drowsiness. In this work, we have applied four different Convolutional Neural Network (CNN) techniques on the YawDD dataset to detect and examine the extent of drowsiness depending on the yawning frequency with specific pose and occlusion variation. Preliminary computational results show that our proposed Ensemble Convolutional Neural Network (ECNN) outperformed the traditional CNN-based approach by achieving an F1 score of 0.935, whereas the other three CNN, such as CNN1, CNN2, and CNN3 approaches gained 0.92, 0.90, and 0.912 F1 scores, respectively.