Eye Gaze Tracking System Research Articles

A robust, real-time camera-based eye gaze tracking system to analyze users’ visual attention using deep learning

ABSTRACT Eye gaze tracking has recently become indispensable for domains like virtual reality, augmented reality, human–computer interaction and advertisement. The commercial eye gaze tracking equipment is too expensive to be used by the masses. In this manuscript, a non-invasive, low-resolution ordinary camera-based system has been proposed for tracking eye gaze. The proposed system attained an accuracy of 84% without requiring any costly equipment or infrared light like other commercial tools. Eye gaze fixations data of 37 participants were recorded on a webpage and gaze patterns were plotted using heat maps. Further, a case analysis was done to validate the performance of the proposed system, where the user interface has been controlled using eye movements.

Remote Eye Gaze Tracking Research: A Comparative Evaluation on Past and Recent Progress

Several decades of eye related research has shown how valuable eye gaze data are for applications that are essential to human daily life. Eye gaze data in a broad sense has been used in research and systems for eye movements, eye tracking, and eye gaze tracking. Since early 2000, eye gaze tracking systems have emerged as interactive gaze-based systems that could be remotely deployed and operated, known as remote eye gaze tracking (REGT) systems. The drop point of visual attention known as point of gaze (PoG), and the direction of visual attention known as line of sight (LoS), are important tasks of REGT systems. In this paper, we present a comparative evaluation of REGT systems intended for the PoG and LoS estimation tasks regarding past to recent progress. Our literature evaluation presents promising insights on key concepts and changes recorded over time in hardware setup, software process, application, and deployment of REGT systems. In addition, we present current issues in REGT research for future attempts.

Convolutional Neural Networks(CNN) based Eye-Gaze Tracking System using Machine Learning Algorithm

To avoid the rising number of car crash deaths, which are mostly caused by drivers' inattentiveness, a paradigm shift is expected. The knowledge of a driver's look area may provide useful details about his or her point of attention. Cars with accurate and low-cost gaze classification systems can increase driver safety. When drivers shift their eyes without turning their heads to look at objects, the margin of error in gaze detection increases. For new consumer electronic applications such as driver tracking systems and novel user interfaces, accurate and effective eye gaze prediction is critical. Such systems must be able to run efficiently in difficult, unconstrained conditions while using reduced power and expense. A deep learning-based gaze estimation technique has been considered to solve this issue, with an emphasis on WSN based Convolutional Neural Networks (CNN) based system. The proposed study proposes the following architecture, which is focused on data science: The first is a novel neural network model that is programmed to manipulate any possible visual feature, such as the states of both eyes and head location, as well as many augmentations; the second is a data fusion approach that incorporates several gaze datasets. However, due to different factors such as environment light shifts, reflections on glasses surface, and motion and optical blurring of the captured eye signal, the accuracy of detecting and classifying the pupil centre and corneal reflection centre depends on a car environment. This work also includes pre-trained models, network structures, and datasets for designing and developing CNN-based deep learning models for Eye-Gaze Tracking and Classification.

Enhancing Reading Advancement Using Eye Gaze Tracking

This research aims to understand the enhancing reading advancement using eye gaze tracking in regards to pull the increase of time interacting with such devices along. In order to realize that, user should have a good understanding of the reading process and of the eye gaze tracking systems; as well as a good understanding of the issues existing while using eye gaze tracking system for reading process. Some issues are very common, so our proposed implementation algorithm compensate these issues. To obtain the best results possible, two mains algorithm have been implemented: the baseline algorithm and the algorithm to smooth the data. The tracking error rate is calculated based on changing points and missed changing points. In [21], a previous implementation on the same data was done and the final tracking error rate value was of 126%. The tracking error rate value seems to be abnormally high but this value is actually useful as described in [21]. For this system, all the algorithms used give a final tracking error rate value of 114.6%. Three main origins of the accuracy of the eye gaze reading were normal fixation, regression, skip fixation; and accuracies are displayed by the tracking rate value obtained. The three main sources of errors are the calibration drift, the quality of the setup and the physical characteristics of the eyes. For the tests, the graphical interface uses characters with an average height of 24 pixels for the text. By considering that the subject was approximately at 60 centimeters of the tracker. The character on the screen represents an angle of ±0.88◦; which is just above the threshold of ±0.5◦ imposed by the physical characteristics of the eyeball for the advancement of reading using eye gaze tracking.

The stability of the MEMS 2D mirror's operating point in terms of eye tracking systems

This paper describes research on the stability of the MEMS mirror for use in eye tracking systems. MEMS mirrors are the main element in scanning methods (which is one of the methods of eye tracking). Due to changes in the mirror pitch, the system can scan the area of the eye with a laser and collect the signal reflected. However, this method works on the assumption that the inclinations are constant in each period. The instability of this causes errors. The aim of this work is to examine the error level caused by pitch instability at different points of work. Full Text: PDF ReferencesW. Fuhl, M. Tonsen, A. Bulling, and E. Kasneci, "Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art," Mach. Vis. Appl., vol. 27, no. 8, pp. 1275-1288, 2016, CrossRef X. Wang, S. Koch, K. Holmqvist, and M. Alexa, "Tracking the gaze on objects in 3D," ACM Trans. Graph., vol. 37, no. 6, pp. 1-18, Dec. 2018 CrossRef X. Xiong and H. Xie, "MEMS dual-mode electrostatically actuated micromirror," Proc. 2014 Zo. 1 Conf. Am. Soc. Eng. Educ. - "Engineering Educ. Ind. Involv. Interdiscip. Trends", ASEE Zo. 1 2014, no. Dmd, 2014 CrossRef E. Pengwang, K. Rabenorosoa, M. Rakotondrabe, and N. Andreff, "Scanning micromirror platform based on MEMS technology for medical application," Micromachines, vol. 7, no. 2, 2016 CrossRef J. P. Giannini, A. G. York, and H. Shroff, "Anticipating, measuring, and minimizing MEMS mirror scan error to improve laser scanning microscopy's speed and accuracy," PLoS One, vol. 12, no. 10, pp. 1-14, 2017 CrossRef C. Hennessey, B. Noureddin, and P. Lawrence, "A single camera eye-gaze tracking system with free head motion," Eye Track. Res. Appl. Symp., vol. 2005, no. March, pp. 87-94, 2005 CrossRef C. H. Morimoto and M. R. M. Mimica, "Eye gaze tracking techniques for interactive applications," Comput. Vis. Image Underst., vol. 98, no. 1, pp. 4-24, Apr. 2005 CrossRef S. T. S. Holmström, U. Baran, and H. Urey, "MEMS laser scanners: A review," J. Microelectromechanical Syst., vol. 23, no. 2, pp. 259-275, 2014 CrossRef C. W. Cho, "Gaze Detection by Wearable Eye-Tracking and NIR LED-Based Head-Tracking Device Based on SVR," ETRI J., vol. 34, no. 4, pp. 542-552, Aug. 2012 CrossRef T. Santini, W. Fuhl, and E. Kasneci, "PuRe: Robust pupil detection for real-time pervasive eye tracking," Comput. Vis. Image Underst., vol. 170, pp. 40-50, May 2018 CrossRef O. Solgaard, A. A. Godil, R. T. Howe, L. P. Lee, Y. A. Peter, and H. Zappe, "Optical MEMS: From micromirrors to complex systems," J. Microelectromechanical Syst., vol. 23, no. 3, pp. 517-538, 2014 CrossRef J. Wang, G. Zhang, and Z. You, "UKF-based MEMS micromirror angle estimation for LiDAR," J. Micromechanics Microengineering, vol. 29, no. 3, 201 CrossRef

An Eye Gaze Tracking Method of Virtual Reality Headset Using A Single Camera and Multi-light Source

For the problems the hardware configuration of multi-camera and multi-light source is complicated and the eye image of little auxiliary light source is too dark in the eye gaze tracking system, A new eye gaze tracking system based on two-dimensional mapping model is proposed, which achieved the transformation from two-dimensional coordinates to three-dimensional coordinates combined attitude angle of virtual reality headset. In this paper, the mapping relationship between visual features and eye gaze points is established by improving the method of pupil image recognition and eye gaze estimation based pupil-corneal, which the accuracy of eye gaze estimation is further improved. The experimental results show that the error of eye gaze tracking system is less than 1.1 °, which achieved the good effect of eye gaze tracking.

A Low-Cost Pupil Center Localization Algorithm Based on Maximized Integral Voting of Circular Hollow Kernels

Abstract Pupil center localization is an essential requirement for robust eye gaze tracking systems. In this paper, a low-cost pupil center localization algorithm is presented. The aim is to propose a computationally inexpensive algorithm with high accuracy in terms of performance and processing speed. Hence, a computationally inexpensive pupil center localization algorithm based on maximized integral voting of candidate kernels is presented. As the kernel type, a novel circular hollow kernel (CHK) is used. Estimation of pupil center is employed by applying a rule-based schema for each pixel over the eye sockets. Additionally, several features of CHK are proposed for maximizing the probability of voting for each kernel. Experimental results show promising results with 96.94% overall accuracy with around 13.89 ms computational time (71.99 fps) for a single image as an average time by using a standard PC. An extensive benchmarking study indicates that this method outperforms the learning-free algorithms and it competes with the other methods having a learning phase while their processing speed is much higher. Furthermore, this proposed learning-free system is fast enough to run on an average PC and also applicable to work with even a low-resolution webcam on a real-time video stream.

An Eye-gaze Tracking System for Teleoperation of a Mobile Robot

Most telerobotic applications rely on a Human-Robot Interface that requires the operator to continuously monitor the state of the robot through visual feedback while uses manual input devices to send commands to control the navigation of the robot. Although this setup is present in many examples of telerobotic applications, it may not be suitable in situations when it is not possible or desirable to have manual input devices, or when the operator has a motor disability that does not allow the use of that type of input devices. Since the operator already uses his/her eyes in the monitoring task, an interface based on the inputs from their gaze could be used to teleoperate the robot. This paper presents a telerobotic platform that uses a user interface based on eye-gaze tracking that enables a user to control the navigation of a teleoperated mobile robot using only his/her eyes as inputs to the system. Details of the operation of the eye-gaze tracking system and the results of a task-oriented evaluation of the developed system are also included.

高齢者の身体的特徴を考慮した視線追跡用Face-Mounted Displayの開発

高齢者の身体的特徴を考慮した視線追跡用Face-Mounted Displayの開発

An augmented image gradients based supervised regression technique for iris center localization

This paper describes a robust and accurate technique for iris center localization by combining supervised regression based approach and image gradients. The proposed work consist of two stages. The first stage comprises regression approach which is based upon learning of local binary features to detect the periocular regions. In the second stage, image gradients were applied to the extracted eye patch regions to detect the accurate iris centers. The proposed augmented image gradients based supervised regression approach tested on the two publicly available challenging datasets show good accuracy. The results proved that supervised regression technique when augmented with image gradients approach improved the accuracy of iris center detection on the face image acquired under unconstraint conditions. The outcome of the proposed work suggests that by augmenting effective unsupervised techniques such as image gradients improves the accuracy and robustness of the supervised approaches used for face alignment applications. This work may be extended towards the development of accurate and fast eye gaze tracking systems.

An easy iris center detection method for eye gaze tracking system

Iris center detection accuracy has great impact on eye gaze tracking system performance. This paper proposes an easy and efficient iris center detection method based on modeling the geometric relationship between the detected rough iris center and the two corners of the eye. The method fully considers four states of iris within the eye region, i.e. center, left, right, and upper. The proposed active edge detection algorithm is utilized to extract iris edge points for ellipse fitting. In addition, this paper also presents a predicted edge point algorithm to solve the decrease in ellipse fitting accuracy, when part of the iris becomes hidden from rolling into a nasal or temporal eye corner. The evaluated result of the method on our eye database shows the global average accuracy of 94.3%. Compared with existing methods, our method achieves the highest iris center detection accuracy. Additionally, in order to test the performance of the proposed method in gaze tracking, this paper presents the results of gaze estimation achieved by our eye gaze tracking system.

A novel approach to the low cost real time eye mouse

The PCCR (Pupil Center Corneal Reflection) method became dominant for finding human's diverse eye gaze directions through the research on the eye tracking technology that has been done for a very long period of time. The initial studies on the eye tracking technology were related to the general human interface for operating equipment and devices, then it has been promoted to the field of various purposes such as a market research in a recent study analyzing customer's behaviors. In particular, a real time eye gaze tracking system is most important for many HCI applications including stereoscopic synthesis, intend extraction, behavior analysis and etc. In order to make an eye gaze tracking system to be real time, the system must have an efficient pupil detection algorithm and ambience-independent image processing as well as reduced complexity, small size and number of circuit components. This paper proposes a method for getting clean images compared to the previous systems to reduce image processing overhead. Because it also helps reducing the number of image frames to be dropped during the image processing, the proposed method can provide a sufficient performance even on a low cost hardware system by reducing the transmission traffic.

A Probabilistic Approach to Online Eye Gaze Tracking Without Explicit Personal Calibration

Existing eye gaze tracking systems typically require an explicit personal calibration process in order to estimate certain person-specific eye parameters. For natural human computer interaction, such a personal calibration is often inconvenient and unnatural. In this paper, we propose a new probabilistic eye gaze tracking system without explicit personal calibration. Unlike the conventional eye gaze tracking methods, which estimate the eye parameter deterministically using known gaze points, our approach estimates the probability distributions of the eye parameter and eye gaze. Using an incremental learning framework, the subject does not need personal calibration before using the system. His/her eye parameter estimation and gaze estimation can be improved gradually when he/she is naturally interacting with the system. The experimental result shows that the proposed system can achieve <3° accuracy for different people without explicit personal calibration.

視線追跡装置による衝動性眼球運動速度の計測

視線追跡装置による衝動性眼球運動速度の計測

3A1-R03 透過式眼鏡型視線検出システムを用いたAndroidアプリケーションの開発(ウェアラブルロボティクス)

3A1-R03 透過式眼鏡型視線検出システムを用いたAndroidアプリケーションの開発(ウェアラブルロボティクス)

Eyeball model-based iris center localization for visible image-based eye-gaze tracking systems

In general, the visible image-based eye-gaze tracking system is heavily dependent on the accuracy of the iris center (IC) localization. In this paper, we propose a novel IC localization method based on the fact that the elliptical shape (ES) of the iris varies according to the rotation of the eyeball. We use the spherical model of the human eyeball and estimate the radius of the iris from the frontal and upright-view image of the eye. By projecting the eyeball rotated in pitch and yaw onto the 2-D plane, a certain number of the ESs of the iris and their corresponding IC locations are generated and registered as a database (DB). Finally, the location of IC is detected by matching the ES of the iris of the input eye image with the ES candidates in the DB. Moreover, combined with facial landmark points-based image rectification, the proposed IC localization method can successfully operate under natural head movement. Experimental results in terms of the IC localization and gaze tracking show that the proposed method achieves superior performance compared with conventional ones.

2A2-G06 瞳位置検出素子の最適配置および瞳位置検出アルゴリズム(感覚・運動・計測(2))

A wide variety of applications featuring eye-gaze tracking systems are expected, such as in communication technology and welfare field. We set dye-sensitized photovoltaic cells on the glasses and determine the pupil position by detecting potential difference from them. In this paper, we make a program that can judge whether the cell positions we set are optimized and make an algorithm to detect horizontal and vertical pupil position by using the least squared method and Newton-Raphson method. With the use of this system, we achieved the line of sight (LOS) detection accuracy of ±2.9° under constant illumination.

Long range eye gaze tracking system for a large screen

Eye gaze tracking system has been widely researched for the replacement of the conventional computer interfaces such as the mouse and keyboard. In this paper, we propose the long range binocular eye gaze tracking system that works from 1.5 m to 2.5 m with allowing a head displacement in depth. The 3D position of the user's eye is obtained from the two wide angle cameras. A high resolution image of the eye is captured using the pan, tilt, and focus controlled narrow angle camera. The angles for maneuvering the pan and tilt motor are calculated by the proposed calibration method based on virtual camera model. The performance of the proposed calibration method is verified in terms of speed and convenience through the experiment. The narrow angle camera keeps tracking the eye while the user moves his head freely. The point-of-gaze (POG) of each eye onto the screen is calculated by using a 2D mapping based gaze estimation technique and the pupil center corneal reflection (PCCR) vector. PCCR vector modification method is applied to overcome the degradation in accuracy with displacements of the head in depth. The final POG is obtained by the average of the two POGs. Experimental results show that the proposed system robustly works for a large screen TV from 1.5 m to 2.5 m distance with displacements of the head in depth (+20 cm) and the average angular error is 0.69°.

Eye gaze patterns in emotional pictures

This paper reports on a preliminary study aiming at investigating the eye gaze pattern and pupil size variation to discriminate emotional states induced by looking at pictures having different arousal content. A wearable and wireless eye gaze tracking system, hereinafter called HATCAM, which was able to robustly detect eye tracking and pupil area was used. A group of ten volunteers was presented with a set of neutral and arousal pictures extracted from the International Affective Picture System according to an ad-hoc experimental protocol. A set of features was extracted from eye gaze patterns and pupil size variations and used to classify the two classes of pictures. Although preliminary, results are very promising for affective computing applications.

AWARENESS EVALUATION OF PATIENTS IN VEGETATIVE STATE EMPLOYING EYE-GAZE TRACKING SYSTEM

Application of eye-gaze tracking system to awareness evaluation is demonstrated. Hitherto awareness evaluation methods are presented. The assumptions of proposed method based on analysis of visual activity of patients in vegetative state are demonstrated. The eye-gaze tracking system "Cyber-Eye" developed at the Multimedia Systems Department employed to conducted experiments is presented. Research described in the paper indicates that awareness level of 13 of 15 tested patients was misdiagnosed before the new method of awareness evaluation is introduced.