A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> C. Calibration <s> This paper presents a general theory for the remote estimation of the point-of-gaze (POG) from the coordinates of the centers of the pupil and corneal reflections. Corneal reflections are produced by light sources that illuminate the eye and the centers of the pupil and corneal reflections are estimated in video images from one or more cameras. The general theory covers the full range of possible system configurations. Using one camera and one light source, the POG can be estimated only if the head is completely stationary. Using one camera and multiple light sources, the POG can be estimated with free head movements, following the completion of a multiple-point calibration procedure. When multiple cameras and multiple light sources are used, the POG can be estimated following a simple one-point calibration procedure. Experimental and simulation results suggest that the main sources of gaze estimation errors are the discrepancy between the shape of real corneas and the spherical corneal shape assumed in the general theory, and the noise in the estimation of the centers of the pupil and corneal reflections. A detailed example of a system that uses the general theory to estimate the POG on a computer screen is presented. <s> BIB001 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> C. Calibration <s> Most available remote eye gaze trackers have two characteristics that hinder them being widely used as the important computer input devices for human computer interaction. First, they have to be calibrated for each user individually; second, they have low tolerance for head movement and require the users to hold their heads unnaturally still. In this paper, by exploiting the eye anatomy, we propose two novel solutions to allow natural head movement and minimize the calibration procedure to only one time for a new individual. The first technique is proposed to estimate the 3D eye gaze directly. In this technique, the cornea of the eyeball is modeled as a convex mirror. Via the properties of convex mirror, a simple method is proposed to estimate the 3D optic axis of the eye. The visual axis, which is the true 3D gaze direction of the user, can be determined subsequently after knowing the angle deviation between the visual axis and optic axis by a simple calibration procedure. Therefore, the gaze point on an object in the scene can be obtained by simply intersecting the estimated 3D gaze direction with the object. Different from the first technique, our second technique does not need to estimate the 3D eye gaze directly, and the gaze point on an object is estimated from a gaze mapping function implicitly. In addition, a dynamic computational head compensation model is developed to automatically update the gaze mapping function whenever the head moves. Hence, the eye gaze can be estimated under natural head movement. Furthermore, it minimizes the calibration procedure to only one time for a new individual. The advantage of the proposed techniques over the current state of the art eye gaze trackers is that it can estimate the eye gaze of the user accurately under natural head movement, without need to perform the gaze calibration every time before using it. Our proposed methods will improve the usability of the eye gaze tracking technology, and we believe that it represents an important step for the eye tracker to be accepted as a natural computer input device. <s> BIB002 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> C. Calibration <s> Abstract Eye movement is a new emerging modality in human computer interfaces. With better access to devices which are able to measure eye movements (so called eye trackers) it becomes accessible even in ordinary environments. However, the first problem that must be faced when working with eye movements is a correct mapping from an output of eye tracker to a gaze point – place where the user is looking at the screen. That is why the work must always be started with calibration of the device. The paper describes the process of calibration, analyses of the possible steps and ways how to simplify this process. <s> BIB003
A generalized structure and model of a human eye is shown in Fig. 4a . The eye parameters typically required in gaze estimation are pupil center, center of curvature of cornea, the optical and the visual axes BIB001 . The posterior of the eyeball is called retina and the center of the retina with highest visual sensitivity is called the Fovea. The line joining the fovea with the center of corneal curvature is called the visual axis. Optical axis is the line passing through the pupil center and center of corneal curvature as shown in Fig 4a. The visual axis determines the direction of gaze and deviates from the optical axis. This offset is known as the kappa angle and measures around 5 degrees BIB002 but is dependent on each user. In gaze estimation, the pan and tilt components of the kappa angle are unique to each user and the visual axis cannot be estimated directly. The visual axis and the kappa angle therefore have to be obtained for each person though a process called calibration which has to be done at the start of an eye tracking procedure. Calibration is performed by showing the user a set of specific targets distributed over the front screen (as shown in Fig. 2 ) and the user is asked to gaze at them for a certain amount of time BIB003 . The tracker camera captures the various eye positions for each target point which are then mapped to the corresponding gaze coordinates and thus the tracker learns this mapping function. Calibration routines differ in the number and layout of target points, user fixation duration at each point and type of mapping algorithm used. 