Abstract
Level 3 automated driving mandates the installation of a driver monitoring system that monitors the driver's condition, such as drowsiness. Conventional drowsiness detection has been hampered by the mental and physical burden of wearing equipment and the time required for detection. In this study, we applied sparse coding to facial images captured with near‐infrared light to extract features of facial skin blood flow information related to drowsiness and to estimate drowsiness. As a result, skin blood flow features related to drowsiness were extracted in the nasal area and other areas, and drowsiness was estimated with 74.6% accuracy. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
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