Novel CNN Based Model Using LSTM for Driver Drowsiness Detection

Drowsiness affects human cognitive functioning, response time, and decision-making capacity, which is a major risk factor in road safety. Drowsiness-caused accidents are increasingly common, which indicates the need for real-time drowsiness detection systems. The purpose of this study is to detect signs of fatigue through facial characteristics such as eye closure, yawning, and tilting of the head in binary and multi-class classification functions. The study proposes a hybrid model, which integrates Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM), which uses a camera-based real -time detection system with DLIB for face tracking. CNN extracts spatial functions, while LSTM preserves temporal dependencies and improves the accuracy of the detection. The experimental result model shows a significant improvement in performance, receiving an accuracy of 96.3%, precision rate of 96.45%, recall rate of 96.33%, and F-measure of 96.32%. The proposed model optimizes feature selection, reduces false alarms, and increases reliability, making this a strong and effective solution to the driver's safety monitoring systems