A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. Platform specific factors affecting usability of gaze tracking systems <s> Abstract Reliable identification of intentional visual commands is a major problem in the development of eye-movements based user interfaces. This work suggests that the presence of focal visual fixations is indicative of visual commands. Two experiments are described which assessed the effectiveness of this approach in a simple gaze-control interface. Identification accuracy was shown to match that of the commonly used dwell time method. Using focal fixations led to less visual fatigue and higher speed of work. Perspectives of using focal fixations for identification of visual commands in various kinds of eye-movements based interfaces are discussed. <s> BIB001 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> B. Platform specific factors affecting usability of gaze tracking systems <s> There is a need to standardize the performance of eye gaze estimation (EGE) methods in various platforms for human computer interaction (HCI). Because of lack of consistent schemes or protocols for summative evaluation of EGE systems, performance results in this field can neither be compared nor reproduced with any consistency. In contemporary literature, gaze tracking accuracy is measured under non-identical sets of conditions, with variable metrics and most results do not report the impact of system meta-parameters that significantly affect tracking performances. In this work, the diverse nature of these research outcomes and system parameters which affect gaze tracking in different platforms is investigated and their error contributions are estimated quantitatively. Then the concept and development of a performance evaluation framework is proposed- that can define design criteria and benchmark quality measures for the eye gaze research community. <s> BIB002
Eye gaze estimation systems on various user platforms and applications face a broad range of operating conditions that are rarely taken into consideration or characterized during their development. The practical performance of a gaze tracking system in reality may be affected by several factors (or we may call them: sources of errors) that are common or unique to each platform. Some of these factors and their effects have been discussed in our earlier work BIB002 . For example, the major sources of errors in desktop are head movements. In head-mounted trackers, errors may arise due to "Midas-touch" effect BIB001 , miss-calibration or tracker latency. Errors in automotive systems may arise from platform and user head movements, variable illumination, occlusion due to shadows or user wearing glasses. In handheld devices, eye tracking gets highly challenged by changing positions of the user with respect to the device, head pose, hand jitter, variable illumination, and Midas-touch. In gaze research, effects of some of these conditions such as head-pose changes, user distance and viewing angle are studied but other factors such as display properties i.e. size and pixel resolution of the screen where gaze is tracked, platform movement and jitter, illumination changes, camera quality and human eye limitations are very sparsely reported. To study this scenario, Table VI presents an information statistics on practical conditions that may affect gaze tracking and the extent to which they have been studied. Firstly the different error sources are listed in Column I. Then the papers are categorized according to platforms to identify the research works where these error sources have been reported. This information is presented in the different columns of Table VI. It is seen from the table that each user platform encounters at least 5-6 different conditions which can affect their performance but their impacts are very sparsely studied for all user platforms. Head-pose is the most studied factor out of all but very few papers analyze the impact of others error sources. Further in presence of these factors, the real performance of an eye tracker can deviate significantly and unpredictably from the scores reported under ideal conditions. Hence, unless these error sources are adequately evaluated, the accuracy achieved by a gaze tracking system cannot be reliably specified.