Inside-Outカメラを用いた畳み込みニューラネットワークに基づく注視点推定

The vision-based gaze estimation system (GES) involves multiple cameras, and such system can estimate gaze direction and what a user is looking at. The inside-out camera is the device to capture user eye and user vision. This system is widely used in many applications because the eye images with the pupil or cornea have much information. These applications have the capability to improve the quality of life of everyone especially a person with a disability. However, an end-user is difficult to access the ability of commercial GES device because of the high price and difficult to use. The budget GES device can be created with a general camera. The common method to estimate the gaze point from the vision-based GES is detected the pupil center position. However, the human eye has variable characteristics and the blinking makes reliable pupil detection is a challenging problem. The state-of-the-art method for the pupil detection is not designed for the wearable camera, the designed for the desktop/TV panels. A small error from the pupil detection can make a large error on gaze point estimation. This thesis presents the novel robust and accurate GES framework by using the learning-based method. The main contributions of this thesis can be divided into two main groups. The first main contribution is to enhance the pupil center detection by creating an effective pupil center detection framework. The second contribution of this thesis is to create the calibration-free GES. The first contribution is to enhance the accuracy of the pupil detection process. Handcraft and learning-based method are used to estimate the pupil center position. We design the handcraft method that using the gradient value and RANSAC ellipse fitting. The pupil center position was estimated by the proposed method and com-pared with the separability filter. The result shows the proposed method has a good performance in term of accuracy and computation time. However, when the user closes the eye, no eye present in the image, or a large unexpected object in the image, the accuracy will be decreased significantly. It is difficult for handcraft method to achieve good accuracy. The learning-based method has the potential to solve the general problem that becomes the focus of this thesis. This thesis presents the convolutional neural network (CNN) model to estimate the pupil position in the various situations. Moreover, this model can recognize the eye states such as open, middle, or closed eyes. The second contribution is to create the calibration-free GES. The calibration process is the process to create the coordinate transfer (CT) function. The CT function uses for transfer the pupil position to the gaze point on-scene image. When the wearable camera moves during the use case, the static CT function cannot estimate the gaze point accurately. The learning-based method has a potential to create a robust and adaptive CT function. The accurate calibration-free system can raise the accuracy of the GES. Furthermore, it makes the GES easy easier to use. We designed the CNN framework that has the ability to estimate the gaze position in the various situations. This thesis also presents the process to create the reliable dataset for GES. The result shows that proposed calibration-free GES can estimation the gaze point when glasses are moved.九州工業大学博士学位論文 学位記番号：情工博甲第338号 学位授与年月日：平成31年3月25日1 Introduction|2 Pupil Detection using handcraft method|3 Convolutional neural network| 4 Pupil detection using CNN method|5 Calibration free approach for GES|6 Character input system|7 Conclusion九州工業大学平成30年

Pupil Position by an Improved Technique of YOLO Network for Eye Tracking Application

This Eye gaze following is the real-time collection of information about a person's eye movements and the direction of their look. Eye gaze trackers are devices that measure the locations of the pupils to detect and track changes in the direction of the user's gaze. There are numerous applications for analyzing eye movements, from psychological studies to human-computer interaction-based systems and interactive robotics controls. Real-time eye gaze monitoring requires an accurate and reliable iris center localization technique. Deep learning technology is used to construct a pupil tracking approach for wearable eye trackers in this study. This pupil tracking method uses deep-learning You Only Look Once (YOLO) model to accurately estimate and anticipate the pupil's central location under conditions of bright, natural light (visible to the naked eye). Testing pupil tracking performance with the upgraded YOLOv7 results in an accuracy rate of 98.50% and a precision rate close to 96.34% using PyTorch

A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images

Deep learning has bolstered gaze estimation techniques, but real-world deployment has been impeded by inadequate training datasets. This problem is exacerbated by both hardware-induced variations in eye images and inherent biological differences across the recorded participants, leading to both feature and pixel-level variance that hinders the generalizability of models trained on specific datasets. While synthetic datasets can be a solution, their creation is both time and resource-intensive. To address this problem, we present a framework called Light Eyes or "LEyes" which, unlike conventional photorealistic methods, only models key image features required for video-based eye tracking using simple light distributions. LEyes facilitates easy configuration for training neural networks across diverse gaze-estimation tasks. We demonstrate that models trained using LEyes are consistently on-par or outperform other state-of-the-art algorithms in terms of pupil and CR localization across well-known datasets. In addition, a LEyes trained model outperforms the industry standard eye tracker using significantly more cost-effective hardware. Going forward, we are confident that LEyes will revolutionize synthetic data generation for gaze estimation models, and lead to significant improvements of the next generation video-based eye trackers.Comment: 32 pages, 8 figure