Eye tracking using Markov models

Abstract

We propose an eye detection and tracking method based on color and geometrical features of the human face using a monocular camera. In this method a decision is made on whether the eyes are closed or not and, using a Markov chain framework to model temporal evolution, the subject's gaze is determined. The method can successfully track facial features even while the head assumes various poses, so long as the nostrils are visible to the camera. We compare our method with recently proposed techniques and results show that it provides more accurate tracking and robustness to variations in view of the face. A procedure for detecting tracking errors is employed to recover the loss of feature points in case of occlusion or very fast head movement. The method may be used in monitoring a driver's alertness and detecting drowsiness, and also in applications requiring non-contact human computer interaction.

Human Eye Localization Using Edge Projections

“…Therefore, it can be applied to human-computer interaction applications, and be used as the initialization stage of eye trackers. In eye tracking applications, e.g., (Bagci et al, 2004), a good initial estimate is satisfactory as the tracker further localizes the positions of eyes. For this reason, d < 0.25 results are more important than those of d < 0.10 from the tracker point of view.…”

Extraction of Eye Region of Human Face Based on Corner Detection and Dilation Operation

“…If we the coordinates of the two eye corners are All these coordinate values, along with Rd and Cd measured for the 10 different images of 10 different classes are shown in Table. 1. In Table. 1, it can be seen that the maximum and minimum values for Rd is 2 (Rdmax) and 0 (Rdmin) respectively and for Cd it's 38 (Cdmax) and 26 (Cdmin) respectively (Bagci et al, 2004). These two values of Cd have been used as the limits or as the distance range for eye distance.…”

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

“…Bagci et al [24] used Markov chain framework to determine whether the eyes are open or closed. The method can be used in monitoring the drivers' alertness as well as in applications which require non-intrusive human computer interactions.…”