CVMar 13, 2023
A XGBoost Algorithm-based Fatigue Recognition Model Using Face Detection
arXiv:2303.12727v12 citationsh-index: 7
Originality Synthesis-oriented
AI Analysis
This addresses fatigue detection for safety applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackled fatigue recognition by constructing an XGBoost-based model using eye and mouth aspect ratios from face detection, achieving an accuracy of 87.37% and sensitivity of 89.14%.
As fatigue is normally revealed in the eyes and mouth of a person's face, this paper tried to construct a XGBoost Algorithm-Based fatigue recognition model using the two indicators, EAR (Eye Aspect Ratio) and MAR(Mouth Aspect Ratio). With an accuracy rate of 87.37% and sensitivity rate of 89.14%, the model was proved to be efficient and valid for further applications.