Noise robustness evaluation of image processing algorithms for eye blinking detection

Robust algorithms for eye blinking detection are required due to the effects of noisy environments and varying light conditions on image-based detection methods. This paper compares five non-supervised image-based algorithms for eye blinking detection, evaluating their robustness to additive Gaussian noise. The algorithms were tested on a video dataset acquired using a smartphone and an ophthalmology chin rest. Through Monte Carlo simulation that introduces Gaussian noise at different intensities, we evaluate the algorithms' precision, sensitivity, and F1-scores for frame classification, True Positive Rate (TPR), False Discovery Rate (FDR) and F1-score for the event detector. The results of experimental tests reveal significant variations in algorithms' performance with increasing noise levels. Notably, the Image Correlation (IC) algorithm demonstrates superior eye blinking detection capabilities under various noise conditions, emerging as the most robust algorithm among those tested. This distinction highlights the potential of IC for reliable blink detection in noisy environments.