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Driver’s drowsiness is one of the major causes of traffic accidents worldwide. An early detection of episodes of sleepiness becomes of fundamental importance for safety purposes. Several studies demonstrated that PERCLOS, that is the percentage of eyelid closure over the pupil across time, is one of the most accurate parameters for drowsiness state assessment. However, since PERCLOS is typically computed from the visible video of the subjects, its evaluation is strictly dependent on the lighting conditions and it is not accessible if the driver wears sunglasses. The objective of this study is to overcome these limitations, evaluating drowsy states using a low-cost and high-resolution thermal infrared technology. Ten sleep-deprived subjects were recruited for the experiment, consisting in one-hour driving task on a driving static simulator. During the experiment, facial skin temperature was recorded by means of the thermal camera Device Alab SmartIr640, together with facial visible videos of the subjects. Relevant thermal features were estimated from facial regions of interest (i.e., nose tip, glabella) whereas PERCLOS was performed on visible videos. Features were extracted over a time window of 30 seconds. A data-driven multivariate machine learning approach based on a three-level Support Vector Classification of the drowsy state (AWAKE class: PERCLOS<0.15, FATIGUE class: 0.15<PERCLOS<0.23, and SLEEPY class: PERCLOS>0.15) was employed. The average classification accuracy was 0.65±0.09 (mean ± standard deviation). Although preliminary, these results indicate the possibility to assess driver's drowsiness based on facial thermal features, overcoming the limitation related to lighting condition and eyes detection, typical of standard methods.
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