A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. Basic setup and method used for eye gaze estimation <s> With recent advances in eye tracking technology, eye gaze gradually gains acceptance as a pointing modality. Its relatively low accuracy, however, determines the need to use enlarged controls in eye-based interfaces rendering their design rather peculiar. Another factor impairing pointing performance is deficient robustness of an eye tracker's calibration. To facilitate pointing at standard-size menus, we developed a technique that uses dynamic target expansion for on-line correction of the eye tracker's calibration. Correction is based on the relative change in the gaze point location upon the expansion. A user study suggests that the technique affords a dramatic six-fold improvement in selection accuracy. This is traded off against a much smaller reduction in performance speed (39%). The technique is thus believed to contribute to development of universal-access solutions supporting navigation through standard menus by eye gaze alone. <s> BIB001 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. Basic setup and method used for eye gaze estimation <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> BIB002 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. Basic setup and method used for eye gaze estimation <s> Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond. <s> BIB003 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. Basic setup and method used for eye gaze estimation <s> We propose a new remote gaze tracking system as an intelligent TV interface. Our research is novel in the following three ways: first, because a user can sit at various positions in front of a large display, the capture volume of the gaze tracking system should be greater, so the proposed system includes two cameras which can be moved simultaneously by panning and tilting mechanisms, a wide view camera (WVC) for detecting eye position and an auto-focusing narrow view camera (NVC) for capturing enlarged eye images. Second, in order to remove the complicated calibration between the WVC and NVC and to enhance the capture speed of the NVC, these two cameras are combined in a parallel structure. Third, the auto-focusing of the NVC is achieved on the basis of both the user’s facial width in the WVC image and a focus score calculated on the eye image of the NVC. Experimental results showed that the proposed system can be operated with a gaze tracking accuracy of ±0.737°~±0.775° and a speed of 5~10 frames/s. <s> BIB004
Video based eye gaze tracking systems comprise fundamentally of one or more digital cameras, near infra-red (NIR) LEDs and a computer with screen displaying a user interface where the user gaze is tracked. A typical eye gaze tracking setup is shown in Fig. 1 . The steps commonly involved in passive video based eye tracking include user calibration, capturing video frames of the face and eye regions of user, eye detection and mapping with gaze coordinates on screen. The common methodology (called Pupil Center Corneal Reflection or PCCR method) involves using NIR LEDs to produce glints on the eye cornea surface and then capturing images/videos of the eye region BIB003 BIB002 . Gaze is estimated from the relative movement between the pupil center and glint positions. External NIR illumination with single/multiple LEDs (wavelengths typically in the range 850+/-30 nm with some works such as BIB004 using 940 nm) is often used to achieve better contrast and avoid effects due to variations induced by natural light. Webcams are mostly used; those operate at 30/60 fps frame rate and have infrared (IR) transmission filters to block out the visible light. Different gaze tracking methods are discussed in detail in Section III. The user-interface for gaze tracking can be active or passive, single or multimodal BIB001 -  . In an active user interface, the user's gaze can be tracked to activate a function and gaze information can be used as an input modality. A passive interface is a non-command interface where eye gaze data is collected to understand user interest or attention. Single modal gaze tracking interfaces use gaze as the only input variable whereas a multimodal interface combines gaze input along with mouse, keyboard, touch, or blink inputs for command. Actual target location Fig.1 .Schematic diagram of a typical gaze tracking system. P is the pupil of the human eye ball and G is the glint location formed on the cornea, which are imaged on the camera plane. The figure also shows the error in gaze estimation as the deviation between actual & estimated gaze locations