01645nas a2200241   4500000000100000000000100001008004100002260001200043653001400055653001500069653002200084653002200106100002300128700002000151700001900171700002000190245009800210856008000308300001200388490000600400520098300406022001401389       2022                            d  c06/202210aEye Blink10aMultimodal10aZ Score Threshold10aWeighted Features1 aPuneet Singh Lamba1 aDeepali Virmani1 aManu S. Pillai1 aGopal Chaudhary00aMultimodal Human Eye Blink Recognition Using Z-score Based Thresholding and Weighted Features  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_9.pdf  a100-1110 v73 aA novel real-time multimodal eye blink detection method using an amalgam of five unique weighted features extracted from the circle boundary formed from the eye landmarks is proposed. The five features, namely (Vertical Head Positioning, Orientation Factor, Proportional Ratio, Area of Intersection, and Upper Eyelid Radius), provide imperative gen (z score threshold) accurately predicting the eye status and thus the blinking status. An accurate and precise algorithm employing the five weighted features is proposed to predict eye status (open/close). One state-of-the-art dataset ZJU (eye-blink), is used to measure the performance of the method. Precision, recall, F1-score, and ROC curve measure the proposed method performance qualitatively and quantitatively. Increased accuracy (of around 97.2%) and precision (97.4%) are obtained compared to other existing unimodal approaches. The efficiency of the proposed method is shown to outperform the state-of-the-art methods.  a1989-1660