A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. TV and large display panels <s> Gaze tracking is used for detecting the position that a user is looking at. In this research, a new gaze-tracking system and method are proposed to control a large-screen TV at a distance. This research is novel in the following four ways as compared to previous work: First, this is the first system for gaze tracking on a large-screen TV at a distance. Second, in order to increase convenience, the user's eye is captured by a remote gaze-tracking camera not requiring a user to wear any device. Third, without the complicated calibrations among the screen, the camera and the eye coordinates, the gaze position on the TV screen is obtained by using a simple 2D method based on a geometric transform with pupil center and four cornea specular reflections. Fourth, by using a near-infrared (NIR) passing filter on the camera and NIR illuminators, the pupil region becomes distinctive in the input image irrespective of the change in the environmental visible light. Experimental results showed that the proposed system could be used as a new interface for controlling a TV with a 60-inch-wide screen (16:9). <s> BIB001 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. TV and large display panels <s> Robust and accurate pupil detection is a prerequisite for gaze detection. Hence, we propose a new eye/pupil detection method for gaze detection on a large display. The novelty of our research can be summarized by the following four points. First, in order to overcome the performance limitations of conventional methods of eye detection, such as adaptive boosting (Adaboost) and continuously adaptive mean shift (CAMShift) algorithms, we propose adaptive selection of the Adaboost and CAMShift methods. Second, this adaptive selection is based on two parameters: pixel differences in successive images and matching values determined by CAMShift. Third, a support vector machine (SVM)-based classifier is used with these two parameters as the input, which improves the eye detection performance. Fourth, the center of the pupil within the detected eye region is accurately located by means of circular edge detection, binarization and calculation of the geometric center. The experimental results show that the proposed method can detect the center of the pupil at a speed of approximately 19.4 frames/s with an RMS error of approximately 5.75 pixels, which is superior to the performance of conventional detection methods. <s> BIB002
There are recent applications of long range gaze estimation that use corneal reflection (CR) techniques for tracking gaze on large displays and smart TVs. Gaze movements can be used to select and navigate menus, modify display properties, switch channels and understand user interests. In Park et al. BIB002 a robust pupil detection method for gaze tracking on large display is presented using a wide and a narrow view camera with Adaboost and CAMShift algorithms. In another work, Park BIB001 reports a system for gaze tracking on a large-screen 60 inch TV based on a 2D method with geometric transform, using pupil center and four corneal specular reflections.