Vision-Based Fatigue Driving Recognition Method Integrating Heart Rate and Facial Features
Bibliographic record
Abstract
Driving fatigue can be detected by measuring drivers' heart rate with a wearable device or extracting their facial features with an RGB camera. However, a wearable device causes inconvenience and discomfort to the driver, and an RGB camera's detection accuracy may be affected by light, glasses, and head orientation. Furthermore, most existing methods ignored the temporal information of fatigue features and the relationship between the features, lowering recognition accuracy. Additionally, some existing fatigue detection methods focused on dealing with fatigue features with a temporal slice, ignoring temporal variations in the features. To address these problems, a single RGB-D camera is first used to extract three fatigue features: heart rate, eye openness level, and mouth openness level. More importantly, this paper proposes a novel multimodal fusion recurrent neural network (MFRNN), integrating the three features to improve the accuracy of driver fatigue detection. Specifically, a recurrent neural network (RNN) layer is applied in the MFRNN to obtain the temporal information of the features. Since the heart rate feature is a physiological signal extracted indirectly, it contains more noise and is fuzzier than the other features. To deal with the fuzziness and noise, we combine fuzzy reasoning with RNN to extract the temporal information of the heart rate. To identify the relationship between the features, we develop a new relationship layer containing a two-level RNN, for which the input is the temporal information of the features. Both the simulation and field experiment results show that the proposed method provides better performance than similar methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".