A Deep Learning Approach to High Accuracy Driver Identification using Physiological Signals with Optimal Driver Pool Size

If drivers can be correctly recognized from their physiological signals, personalized services such as dynamical adjustments to the seat, backrest, and headrest can be provided during driving to ensure comfort and safety. However, such services require high accuracy identification of drivers, i.e., identification accuracy more than 99%. To determine whether such high accuracy can be achieved using drivers' physiological signals, we propose identifying only a limited group of drivers for a particular vehicle. Specifically, we built two deep learning models from three common physiological signals. To achieve high accuracy identification of drivers, we adjusted the size of the drivers to be identified (driver pool) to achieve a high identification accuracy of drivers. Our results show that when the maximum driver pool sizes are 5 and 2, the identification accuracies reached 95% and 99%, respectively.