A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> This work explores a new direction in utilizing eye gaze forcomputer input. Gaze tracking has long been considered as analternative or potentially superior pointing method for computerinput. We believe that many fundamental limitations exist withtraditional gaze pointing. In particular, it is unnatural tooverload a perceptual channel such as vision with a motor controltask. We therefore propose an alternative approach, dubbed MAGIC(Manual And Gaze Input Cascaded) pointing. With such an approach,pointing appears to the user to be a manual task, used for finemanipulation and selection. However, a large portion of the cursormovement is eliminated by warping the cursor to the eye gaze area,which encompasses the target. Two specific MAGIC pointingtechniques, one conservative and one liberal, were designed,analyzed, and implemented with an eye tracker we developed. Theywere then tested in a pilot study. This early- stage explorationshowed that the MAGIC pointing techniques might offer manyadvantages, including reduced physical effort and fatigue ascompared to traditional manual pointing, greater accuracy andnaturalness than traditional gaze pointing, and possibly fasterspeed than manual pointing. The pros and cons of the two techniquesare discussed in light of both performance data and subjectivereports. <s> BIB001 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> Eye gaze interaction can provide a convenient and natural addition to user-computer dialogues. We have previously reported on our interaction techniques using eye gaze [10]. While our techniques seemed useful in demonstration, we now investigate their strengths and weaknesses in a controlled setting. In this paper, we present two experiments that compare an interaction technique we developed for object selection based on a where a person is looking with the most commonly used selection method using a mouse. We find that our eye gaze interaction technique is faster than selection with a mouse. The results show that our algorithm, which makes use of knowledge about how the eyes behave, preserves the natural quickness of the eye. Eye gaze interaction is a reasonable addition to computer interaction and is convenient in situations where it is important to use the hands for other tasks. It is particularly beneficial for the larger screen workspaces and virtual environments of the future, and it will become increasingly practical as eye tracker technology matures. <s> BIB002 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> Users of today’s desktop interface often suffer from interruption overload. Our research seeks to develop an attention manager that mitigates the disruptive effects of interruptions by identifying moments of low mental workload in a user’s task sequence. To develop such a system, however, we need effective mechanisms to identify user tasks in real-time. In this paper, we show how eye gaze patterns may be used to identify user tasks. We also show that gaze patterns can indicate usability issues of an interface as well as the mental workload that the interface induces on a user. Our results can help inform the design of an attention manager and may lead to new methods to evaluate user interfaces. <s> BIB003 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> Shoulder-surfing -- using direct observation techniques, such as looking over someone's shoulder, to get passwords, PINs and other sensitive personal information -- is a problem that has been difficult to overcome. When a user enters information using a keyboard, mouse, touch screen or any traditional input device, a malicious observer may be able to acquire the user's password credentials. We present EyePassword, a system that mitigates the issues of shoulder surfing via a novel approach to user input. With EyePassword, a user enters sensitive input (password, PIN, etc.) by selecting from an on-screen keyboard using only the orientation of their pupils (i.e. the position of their gaze on screen), making eavesdropping by a malicious observer largely impractical. We present a number of design choices and discuss their effect on usability and security. We conducted user studies to evaluate the speed, accuracy and user acceptance of our approach. Our results demonstrate that gaze-based password entry requires marginal additional time over using a keyboard, error rates are similar to those of using a keyboard and subjects preferred the gaze-based password entry approach over traditional methods. <s> BIB004 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> To evaluate the potential of gaze input for game interaction, we used two tasks commonly found in video game control, target acquisition and target tracking, in a set of two experiments. In the first experiment, we compared the target acquisition and target tracking performance of two eye trackers with four other input devices. Gaze input had a similar performance to the mouse for big targets, and better performance than a joystick, a device often used in gaming. In the second experiment, we compared target acquisition performance using either gaze or mouse for pointing, and either a mouse button or an EMG switch for clicking. The hands-free gaze-EMG input combination was faster than the mouse while maintaining a similar error rate. Our results suggest that there is a potential for gaze input in game interaction, given a sufficiently accurate and responsive eye tracker and a well-designed interface. <s> BIB005 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> Future intelligent environments and systems may need to interact with humans while simultaneously analyzing events and critical situations. Assistive living, advanced driver assistance systems, and intelligent command-and-control centers are just a few of these cases where human interactions play a critical role in situation analysis. In particular, the behavior or body language of the human subject may be a strong indicator of the context of the situation. In this paper we demonstrate how the interaction of a human observer's head pose and eye gaze behaviors can provide significant insight into the context of the event. Such semantic data derived from human behaviors can be used to help interpret and recognize an ongoing event. We present examples from driving and intelligent meeting rooms to support these conclusions, and demonstrate how to use these techniques to improve contextual learning. <s> BIB006 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> With computers being used ever more ubiquitously in situations where privacy is important, secure user authentication is a central requirement. Gaze-based graphical passwords are a particularly promising means for shoulder-surfing-resistant authentication, but selecting secure passwords remains challenging. In this paper, we present a novel gaze-based authentication scheme that makes use of cued-recall graphical passwords on a single image. In order to increase password security, our approach uses a computational model of visual attention to mask those areas of the image that are most likely to attract visual attention. We create a realistic threat model for attacks that may occur in public settings, such as filming the user's interaction while drawing money from an ATM. Based on a 12-participant user study, we show that our approach is significantly more secure than a standard image-based authentication and gaze-based 4-digit PIN entry. <s> BIB007 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> The field of Human-Computer Interaction (HCI) has witnessed a tremendous growth in the past decade. The advent of tablet PCs and cell phones allowing touch-based control has been hailed warmly. The researchers in this field have also explored the potential of `eye-gaze' as a possible means of interaction. Some commercial solutions have already been launched, but they are as yet expensive and offer limited usability. This paper strives to present a low cost real time system for eye-gaze based human-computer interaction. <s> BIB008 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> PINs are one of the most popular methods to perform simple and fast user authentication.PIN stands for Personal Identification Number, which may have any number of digits or even letters.Nevertheless, 4-digit PIN is the most common and is used for instance in ATMs or cellular phones.The main advantage of the PIN is that it is easy to remember and fast to enter. There are, however,some drawbacks. One of them - addressed in this paper - is a possibility to steal PIN by a techniquecalled `shoulder surfing'. To avoid such problems a novel method of the PIN entering was proposed.Instead of using a numerical keyboard, the PIN may be entered by eye gazes, which is a hands-free,easy and robust technique. <s> BIB009 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> A. Desktop based systems <s> In order to enhance the practicability and accuracy of the eye-gaze tracking system, a new type low pixel eye feature point location method is adopted. This method can accurately extract the eye-gaze features, namely iris centre point and canthus points when the image pickup requirements are low. The eye-gaze tracking method based on particle swarm optimization (PSO) BP neural network is raised, to capture pictures of eyes under the same environment, and a regression model where the connection weights and threshold values are optimized by PSO algorithm is built via BP network. This method is free of the inherent defects of BP network. This method requires only a common camera and normal illumination intensity rather than high-standard hardware, which greatly cuts the restrictive requirements for the system hardware and thus enhances the system practicability. The experiment results show that PSO-BP model is of higher robustness and accuracy than BP model, and is of higher recognition rate and can effectively enhances the eye-gaze tracking accuracy. <s> BIB010
Applications of eye gaze on desktop systems fall into several categories, such as computer communication, password entry and psychoanalysis. Sibert and Jacob BIB002 developed gaze based fast object selection as a substitute for mouse. A gaze based application called MAGIC (Manual And Gaze Input Cascaded) is presented by Morimoto BIB001 in which gaze based pointing is reported to have higher speed and accuracy than manual pointing. In Ghani BIB008 , a Hough transform based pupil detection for gaze based control of a mouse pointer is proposed. In Villanueva BIB005 evaluation studies on the use of eye gaze in video gaming control for target acquisition and tracking are made. Gaze input had a similar performance to the mouse and joysticks for big targets. Kasprowsky BIB009 and Kumar BIB004 reported the use of gaze to enter a password using gaze tracking. A series of user fixations on specified digits formed the password sequence. Methods robust to shoulder surfing Image databases < 7 deg Head pose variations, occlusion BIB010 Commercial camera 94 % ------problem were reported by Bulling BIB007 where a computational model of visual attention is used to increase security. Applications of using eye gaze patterns for identifying user tasks, mental workload and inferring context of events and user distraction were reported by Iqbal BIB003 and Doshi BIB006 .