Selection of unique gaze direction based on pupil position

Abstract

The ‘gaze estimation’ problem, because of its manifold applications including human–computer interaction especially for the handicapped, has been a topic of research for many years. Recently, thanks to technological advances, non‐intrusive methods based on image processing employed in broader applications, are addressed more than before. One of the promising approaches to gaze estimation is based on projective geometry. In projective geometry‐based approaches, an ellipse is fitted to the image of iris boundary and from its parameters, two solutions are obtained, only one of which is valid for the gaze direction. Since the ellipse parameters are not adequate for disambiguation, in a previous work, the accurate coordinates of eye corners in three‐dimensional, as complementary information, is obtained through another system increasing the cost and complexity of the overall system. In this article, a new technique to select the valid solution, based on eye geometry and iris image, is developed. In the proposed technique, relative position of the pupil centre with respect to the iris centre is used as the complementary information and a novel algorithm is proposed for its extraction. The performance of the proposed technique was evaluated on 600 real images and with only one failure on selecting the valid solution demonstrated an accuracy of 99.8% for disambiguation.

2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network

“…According to differences in dimension of gaze direction estimation, gaze tracking technology can be divided into 2D gaze tracking [11][12][13][14][15][16][17][18][19] and 3D gaze tracking [20][21][22][23][24][25][26][27]; according to differences in ways of wearing, gaze tracking technology can be classed as intrusive (head-mounted) [12,[28][29][30][31][32][33][34][35][36][37] or non-intrusive (head-free) [20,23,[38][39][40][41][42][43][44]. For different gaze tracking systems, gaze tracking methods mainly contain Limbus Tracking [45][46][47], Pupil Tracking [48][49][50], Pupil-glint Vector [51][52][53][54][55], ...…”

A fast iris localization using inversion transform and restricted circular Hough transform

“…Dey [9] proposed scaling and color level transform followed by thresholding for pupil boundary detection and dilation, thresholding and vertical edge detection for iris boundary detection. A gaze estimation based on projective geometry approaches and elliptic curve fitting methods to find centers and radii for pupil and iris boundaries was proposed by Mohammadi [21]. Kumar et al [28] proposed iris segmentation framework for near infrared or visible illuminate iris images based on classification of pixel dependencies between iris and non-iris regions.…”