Eye detection is a pre-requisite stage for many applications such as human–computer interfaces, iris recognition, driver drowsiness detection, security, and biology systems. In this paper, template based eye detection is described. The template is correlated with different regions of the face image. The region of face which gives maximum correlation with template refers to eye region. The metho...

The algorithm based physiological characteristics of driver drowsiness – ocular parameters (derived from the frontal electroencephalogram (EEG)), EEG alpha bursts and spectral power (derived from the central and occipital sites) as well as heart rate variability (HRV) were estimated from data derived during a driving simulator experiment (30 non-professional drivers). The statistical associatio...

Considerable progress has been made in measuring drowsiness and understanding its effects upon human performance in the laboratory and in simulated and operational driving conditions. This work builds upon previous research and identifies an appropriate design for a drowsy driver detection interface. A participatory design process was used that included both design experts and drivers in separa...

Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, approach to detect drowsiness drivers presented, focusing on the eye region, since fatigue one of first symptoms drowsiness. The method used for extraction region Mediapipe, chosen its high accuracy robustness. Three neural networks were analyzed based InceptionV3, VGG16 R...

The aim of the research is to detect driving fatigue and determine whether driver sleeping or awake. There are several approaches for detecting tiredness based on extensive learning, but in this instance we will use machine learning algorithms. alarm sounds when becomes sleepy prevent accidents. Factors compared using various groups, 20 samples used obtain an 80 percent Gpower accuracy rate. EA...