A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Need and rationale for developing comprehensive performance evaluation strategies for gaze estimation systems <s> We describe a system designed to monitor the gaze of a user working naturally at a computer workstation. The system consists of three cameras situated between the keyboard and the monitor. Free head movements are allowed within a three-dimensional volume approximately 40 centimeters in diameter. Two fixed, wide-field "face" cameras equipped with active-illumination systems enable rapid localization of the subject's pupils. A third steerable "eye" camera has a relatively narrow field of view, and acquires the images of the eyes which are used for gaze estimation. Unlike previous approaches which construct an explicit three-dimensional representation of the subject's head and eye, we derive mappings for steering control and gaze estimation using a procedure we call implicit calibration. Implicit calibration is performed by collecting a "training set" of parameters and associated measurements, and solving for a set of coefficients relating the measurements back to the parameters of interest. Preliminary data on three subjects indicate an median gaze estimation error of ap-proximately 0.8 degree. <s> BIB001 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Need and rationale for developing comprehensive performance evaluation strategies for gaze estimation systems <s> This paper describes a computer vision system based on active IR illumination for real-time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using generalized regression neural networks (GRNNs). With GRNNs, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. To further improve the gaze estimation accuracy, we employ a hierarchical classification scheme that deals with the classes that tend to be misclassified. This leads to a 10% improvement in classification error. The angular gaze accuracy is about 5° horizontally and 8° vertically. The effectiveness of our gaze tracker is demonstrated by experiments that involve gaze-contingent interactive graphic display. <s> BIB002 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Need and rationale for developing comprehensive performance evaluation strategies for gaze estimation systems <s> Human eye gaze is a strong candidate to create a new application area based on human-computer interaction. To implement a really practical gaze-based interaction system, gaze detection must be realized without placing any restriction on the user's behavior or comfort. This paper describes a gaze tracking system that offers freehead, simple personal calibration. It does not require the user wear anything on her head, and she can move her head freely. Personal calibration takes only a very short time; the user is asked to look at two markers on the screen. An experiment shows that the accuracy of the implemented system is about 1.0 degrees (view angle). <s> BIB003 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Need and rationale for developing comprehensive performance evaluation strategies for gaze estimation systems <s> Many eye-tracking systems either require the user to keep their head still or involve cameras or other equipment mounted on the user's head. While acceptable for research applications, these limitations make the systems unsatisfactory for prolonged use in interactive applications. Since the goal of our work is to use eye trackers for improved visual communication through gaze guidance [1,2] and for Augmentative and Alternative Communication (AAC) [3], we are interested in less invasive eye tracking techniques. <s> BIB004 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Need and rationale for developing comprehensive performance evaluation strategies for gaze estimation systems <s> One of the main limitations of current remote eye gaze tracking (REGT) techniques is that the user's head must remain within a very limited area in front of the monitor screen. In this paper, we present a free head motion REGT technique. By projecting a known rectangular pattern of lights, the technique estimates the gaze position relative to this rectangle using an invariant property of projective geometry. We carry extensive analysis of similar methods using an eye model to compare their accuracy. Based on these results, we propose a new estimation procedure that compensates the angular difference between the eye visual axis and optical axis. We have developed a real time (30 fps) prototype using a single camera and 5 light sources to generate the light pattern. Experimental results shows that the accuracy of the system is about 1deg of visual angle <s> BIB005 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Need and rationale for developing comprehensive performance evaluation strategies for gaze estimation systems <s> We investigate the appearance-based gaze estimation problem, with respect to its essential difficulty in reducing the number of required training samples, and other practical issues such as slight head motion, image resolution variation, and eye blinking. We cast the problem as mapping high-dimensional eye image features to low-dimensional gaze positions, and propose an adaptive linear regression (ALR) method as the key to our solution. The ALR method adaptively selects an optimal set of sparsest training samples for the gaze estimation via l ::: 1 ::: -optimization. In this sense, the number of required training samples is significantly reduced for high accuracy estimation. In addition, by adopting the basic ALR objective function, we integrate the gaze estimation, subpixel alignment and blink detection into a unified optimization framework. By solving these problems simultaneously, we successfully handle slight head motion, image resolution variation and eye blinking in appearance-based gaze estimation. We evaluated the proposed method by conducting experiments with multiple users and variant conditions to verify its effectiveness. <s> BIB006 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Need and rationale for developing comprehensive performance evaluation strategies for gaze estimation systems <s> The remote gaze estimation (RGE) technique has been widely used as a natural interface in consumer electronic devices for decades. Although outstanding outcomes on RGE have been recently reported in the literature, tracking gaze under large head movements is still an unsolved problem. General RGE methods estimate a user’s point of gaze (POG) using a mapping function representing the relationship between several infrared light sources and their corresponding corneal reflections (CRs) in the eye image. However, the minimum number of available CRs required for a valid POG estimation cannot be satisfied in those methods because the CRs often tend to be distorted or disappeared inevitably under the unconstrained eye and head movements. To overcome this problem, a multiple-transform-based method is proposed. In the proposed method, through three different geometric transform-based normalization processes, several nonlinear mapping functions are simultaneously obtained in the calibration process and then used to estimate the POG. The geometric transforms and mapping functions can be alternatively employed according to the number of available CRs even under large head movement. Experimental results on six subjects demonstrate the effectiveness of the proposed method. <s> BIB007
From the sections and tables above it is seen that currently there exists a large diversity among gaze tracking methods, setup, implementation platforms and accuracy metrics. An important issue that becomes apparent is that there is currently no comprehensive practice for realistic performance evaluation of gaze tracking systems. Most research works do not assess their systems under the impact of various error sources or provide adequate details about their system configurations. For example, Table VI shows that only 35 out of 69 papers on desktop based systems and 16 out of 57 papers on head-mounted systems report effect of head pose variations. Only 2 works in each of desktop and head-mounted platforms report effect of display properties of the gaze tracking setup. Effect of illumination changes is reported in 4 papers in desktop and one in head-mounted systems. In dynamic platforms like automotive and hand-held devices, where external conditions are more variable, the evaluation statistics is even poorer, as seen from Table VI. Another issue observed is that the accuracy scores for gaze based systems are presented using varied formats and metrics, such as angular resolution, correct detection rate, pixel and physical distances etc. which makes them difficult to interpret and inter-compare. For example: Table V and Fig. 10 show that out of 182 total research works across all platforms, 95 papers report gaze accuracy scores in degrees whereas 41 papers report in percentages (correct detection rates) and 46 papers use other heterogeneous units. There is also considerable ambiguity in terminologies used in eye gaze research. For example, mentioning "slight head movement" (as in  , BIB006 , BIB001 ) or "large head movement" (in BIB007 ,  , BIB002 ) and free head movement (in BIB003 , BIB004 , BIB005 ) gives no quantitative idea about the real extent of head pose variations tolerated by these gaze tracking systems. Considering these factors, the development of comprehensive evaluation strategies for gaze tracking systems seems necessary for several reasons: a. to study impact of various error sources on system performance b. to report system performance quantitatively in uniform formats c. compare results from different eye tracking systems under different operating conditions d. to identify the main bottlenecks for each platform. We present here the concept of such an evaluation framework in the sections below.