Facial Feature Recognition Model for the Sleepiness Detection of the Drivers

Abstract

Facial feature recognition is a pivotal technology in computer vision, enabling accurate identification and analysis of human faces by extracting and analyzing key facial landmarks and attributes. This process involves detecting features such as eyes, nose, mouth, and jawline, as well as capturing finer details like textures, edges, and contours. Advanced techniques leverage machine learning and deep learning algorithms, including convolutional neural networks (CNNs) and deep feature extraction models, to enhance accuracy and robustness. Applications of facial feature recognition span a wide range of domains, from security systems and identity verification to emotion detection, healthcare diagnostics, and personalized user experiences. This study presents the LGFR-DL model, a high-performance deep learning framework designed for accurate classification of drowsiness states in real-time applications. The model effectively identifies Awake, Drowsy, and Sleepy (Critical) states with accuracy levels of 98.5%, 90.0%, and 94.0%, respectively, while maintaining high precision, recall, and F1-scores across all categories. Leveraging fused feature extraction, the LGFR-DL model outperforms traditional CNN and DNN models, achieving a superior ROC-AUC of 0.98 and minimal validation loss of 0.075. With a low latency of 42 ms and robust generalization, the model is optimized for real-world applications like driver monitoring systems. This work underscores the potential of LGFR-DL in advancing safety-critical systems by providing reliable and efficient drowsiness detection, paving the way for improved accident prevention and enhanced operational security.