scholarly journals Low Cost Eye Tracking: The Current Panorama

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Onur Ferhat ◽  
Fernando Vilariño
Keyword(s):  
Eye Tracking ◽  
Visible Light ◽  
Open Source ◽  
Low Cost ◽  
Gaze Estimation ◽  
Head Pose ◽  
Gaze Tracking ◽  
Estimation Techniques ◽  
Public Datasets ◽  
Interesting Alternative

Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools.

scholarly journals Real-time gaze estimation via pupil center tracking

2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Natural Environments ◽  
Gaze Estimation ◽  
Head Pose ◽  
Data Set ◽  
Gaze Tracking ◽  
Illumination Changes ◽  
Wide Range ◽  
Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

scholarly journals RGBD Based Gaze Estimation via Multi-Task CNN

2019 ◽  
Vol 33 ◽  
pp. 2488-2495 ◽  
Author(s):  
Dongze Lian ◽  
Ziheng Zhang ◽  
Weixin Luo ◽  
Lina Hu ◽  
Minye Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Image Synthesis ◽  
Position Estimation ◽  
Point Estimation ◽  
Head Pose Estimation ◽  
Depth Image ◽  
Eye Position ◽  
Gaze Estimation ◽  
Head Pose ◽  
Gaze Tracking

This paper tackles RGBD based gaze estimation with Convolutional Neural Networks (CNNs). Specifically, we propose to decompose gaze point estimation into eyeball pose, head pose, and 3D eye position estimation. Compared with RGB image-based gaze tracking, having depth modality helps to facilitate head pose estimation and 3D eye position estimation. The captured depth image, however, usually contains noise and black holes which noticeably hamper gaze tracking. Thus we propose a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points. We utilize a generator network for depth image generation with a Generative Neural Network (GAN), where the generator network is partially shared by both the gaze tracking network and GAN-based depth synthesizing. By optimizing the whole network simultaneously, depth image synthesis improves gaze point estimation and vice versa. Since the only existing RGBD dataset (EYEDIAP) is too small, we build a large-scale RGBD gaze tracking dataset for performance evaluation. As far as we know, it is the largest RGBD gaze dataset in terms of the number of participants. Comprehensive experiments demonstrate that our method outperforms existing methods by a large margin on both our dataset and the EYEDIAP dataset.

scholarly journals Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 543 ◽  
Author(s):  
Braiden Brousseau ◽  
Jonathan Rose ◽  
Moshe Eizenman
Keyword(s):  
Eye Tracking ◽  
Tracking System ◽  
Low Cost ◽  
Center Of Mass ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Gaze Estimation ◽  
Estimation Model ◽  
Automotive Safety ◽  
Artificial Illumination

This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in domains such as neurological and neuropsychiatric testing, advertising evaluation, pilot training, and automotive safety. Remote eye-tracking on a smartphone could enable the significant growth in the deployment of applications in these domains. Our system uses a 3D gaze-estimation model that enables accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features (pupil center and corneal reflections), the system uses Convolutional Neural Networks (CNNs) together with a novel center-of-mass output layer. The use of CNNs improves the system’s robustness to the significant variability in the appearance of eye-images found in handheld eye trackers. The system was tested with 8 subjects with the device free to move in their hands and produced a gaze bias of 0.72°. Our hybrid approach that uses artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor achieved an accuracy that is significantly (400%) better than current eye-tracking systems on smartphones that use natural illumination and machine-learning techniques to estimate the PoG.

scholarly journals EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking

2021 ◽  
Vol 15 ◽  
Author(s):  
Simon Arvin ◽  
Rune Nguyen Rasmussen ◽  
Keisuke Yonehara
Keyword(s):  
Eye Tracking ◽  
Open Source ◽  
High Speed ◽  
Closed Loop ◽  
Low Cost ◽  
Open Loop ◽  
Eye Tracker ◽  
High Resource ◽  
Real Time Analysis ◽  
Loop Design

Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.

scholarly journals Low-Cost Eye Tracking Calibration: A Knowledge-Based Study

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5109
Author(s):  
Gonzalo Garde ◽  
Andoni Larumbe-Bergera ◽  
Benoît Bossavit ◽  
Sonia Porta ◽  
Rafael Cabeza ◽  
...  
Keyword(s):  
Eye Tracking ◽  
High Performance ◽  
Low Cost ◽  
Image Features ◽  
Gaze Estimation ◽  
Low Resolution ◽  
Knowledge Based ◽  
Fully Connected ◽  
The Impact ◽  
Selection Of

Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7∘, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.

scholarly journals A Deep Learning-Based Approach to Video-Based Eye Tracking for Human Psychophysics

2021 ◽  
Vol 15 ◽  
Author(s):  
Niklas Zdarsky ◽  
Stefan Treue ◽  
Moein Esghaei
Keyword(s):  
Deep Learning ◽  
Eye Tracking ◽  
Visual Angle ◽  
Low Cost ◽  
Infrared Camera ◽  
Learning Approaches ◽  
Gaze Tracking ◽  
Points Of Interest ◽  
Video Frames ◽  
Median Error

Real-time gaze tracking provides crucial input to psychophysics studies and neuromarketing applications. Many of the modern eye-tracking solutions are expensive mainly due to the high-end processing hardware specialized for processing infrared-camera pictures. Here, we introduce a deep learning-based approach which uses the video frames of low-cost web cameras. Using DeepLabCut (DLC), an open-source toolbox for extracting points of interest from videos, we obtained facial landmarks critical to gaze location and estimated the point of gaze on a computer screen via a shallow neural network. Tested for three extreme poses, this architecture reached a median error of about one degree of visual angle. Our results contribute to the growing field of deep-learning approaches to eye-tracking, laying the foundation for further investigation by researchers in psychophysics or neuromarketing.

scholarly journals Automatic pupil detection and gaze estimation using the vestibulo-ocular reflex in a low-cost eye-tracking setup

2020 ◽  
Vol 111 (3) ◽  
pp. 120-124 ◽  
Author(s):  
Vered Aharonson ◽  
Verushen Y. Coopoo ◽  
Kyle L. Govender ◽  
Michiel Postema
Keyword(s):  
Eye Tracking ◽  
Low Cost ◽  
Gaze Estimation ◽  
Ocular Reflex ◽  
Pupil Detection ◽  
Vestibulo Ocular Reflex
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