Real-time eyelid open/closed state recognition based on HLAC towards driver drowsiness detection

This paper proposes a real-time eyelid state recognition method based on a video sequence. The human eye strongly reflects the mental state of an individual, such as attention, drowsiness, stress and confusion. In recent times, the automatic identification of such mental states using non-contact eyelid state recognition technology is proving to be a promising avenue for the development of such systems. In the field of Intelligent Transport Systems (ITS), high accuracy and real-time processing are necessary for detecting driver drowsiness in order to prevent accidents. To develop such a recognition method, we use Higher-order Local Auto-Correlation (HLAC). HLAC can represent the shape feature in images clearly without incurring a high computational cost, and it implements position invariability. A Support Vector Machine (SVM) is used to differentiate between open and closed eyelids on the basis of the HLAC feature. The proposed method can achieve open and closed eyelid recognition rates of 98.77% and 98.98%, respectively. We also verify the real-time processing capabilities of our method, thus confirming that it is effective for eyelid state recognition.