Accurate drowsiness estimation via eye-related movements: a neural-network-based investigation

Abstract

Many studies reported eye-related movements, e.g., eye blink and eyelid drooping, are highly indicative symptoms of drowsiness. However, few has investigated the computational efficacy for drowsiness estimation accounted by these movements. This paper thus analyzes two typical movements: eyelid movements and eyeball movements, and investigates different neural-network modelings: CNN-Net and CNN-LSTM-Net. Experimental results show that using joint movements can achieve better performances than eyelid movements for short time drowsiness estimation while using eyeball movements alone perform worse even than the baseline (PERCLOS method). In addition, the CNN-Net is more effective for accurate drowsiness level estimation than the CNN-LSTM-Net.