skip to main content

Explore Research Products in the NSF-PAR

Title: EyeSyn: Psychology-inspired Eye Movement Synthesis for Gaze-based Activity Recognition
Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition. To achieve state-of-the-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers. However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect. To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset. Taking gaze-based museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct pat-terns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from different measurement setups and subject heterogeneity. Moreover, in the few-shot learning scenario, EyeSyn can be readily incorporated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training.  more » « less
Award ID(s):
2046072 1908051 1903136
NSF-PAR ID:
10407098
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ACM/IEEE IPSN
Page Range / eLocation ID:
233 to 246
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ( , Proceedings of the 18th Conference on Embedded Networked Sensor Systems)
    null (Ed.)
    In this work, we present GazeGraph, a system that leverages human gazes as the sensing modality for cognitive context sensing. GazeGraph is a generalized framework that is compatible with different eye trackers and supports various gaze-based sensing applications. It ensures high sensing performance in the presence of heterogeneity of human visual behavior, and enables quick system adaptation to unseen sensing scenarios with few-shot instances. To achieve these capabilities, we introduce the spatial-temporal gaze graphs and the deep learning-based representation learning method to extract powerful and generalized features from the eye movements for context sensing. Furthermore, we develop a few-shot gaze graph learning module that adapts the `learning to learn' concept from meta-learning to enable quick system adaptation in a data-efficient manner. Our evaluation demonstrates that GazeGraph outperforms the existing solutions in recognition accuracy by 45% on average over three datasets. Moreover, in few-shot learning scenarios, GazeGraph outperforms the transfer learning-based approach by 19% to 30%, while reducing the system adaptation time by 80%. 
    more » « less
  2. ; ; ; ; ; ( , Proccedings of the 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR))
    Eye tracking has already made its way to current commercial wearable display devices, and is becoming increasingly important for virtual and augmented reality applications. However, the existing model-based eye tracking solutions are not capable of conducting very accurate gaze angle measurements, and may not be sufficient to solve challenging display problems such as pupil steering or eyebox expansion. In this paper, we argue that accurate detection and localization of pupil in 3D space is a necessary intermediate step in model-based eye tracking. Existing methods and datasets either ignore evaluating the accuracy of 3D pupil localization or evaluate it only on synthetic data. To this end, we capture the first 3D pupilgaze-measurement dataset using a high precision setup with head stabilization and release it as the first benchmark dataset to evaluate both 3D pupil localization and gaze tracking methods. Furthermore, we utilize an advanced eye model to replace the commonly used oversimplified eye model. Leveraging the eye model, we propose a novel 3D pupil localization method with a deep learning-based corneal refraction correction. We demonstrate that our method outperforms the state-of-the-art works by reducing the 3D pupil localization error by 47.5% and the gaze estimation error by 18.7%. Our dataset and codes can be found here: link. 
    more » « less
  3. ; ; ; ; ; ( , 2018 IEEE International Symposium on Technologies for Homeland Security (HST))
    which can assure the security of the country boarder and aid in search and rescue missions. This paper offers a novel “handsfree” tool for aerial border surveillance, search and rescue missions using head-mounted eye tracking technology. The contributions of this work are: i) a gaze based aerial boarder surveillance object classification and recognition framework; ii) real-time object detection and identification system in nonscanned regions; iii) investigating the scan-path (fixation and non-scanned) provided by mobile eye tracker can help improve training professional search and rescue organizations or even artificial intelligence robots for searching and rescuing missions. The proposed system architecture is further demonstrated using a dataset of large-scale real-life head-mounted eye tracking data. Keywords—Head-mounted eye tracking technology, Aerial border surveillance, and search and rescue missions 
    more » « less
  4. ; ; ; ( , International Conference on Artificial Reality and Telexistence Eurographics Symposium on Virtual Environments (ICAT-EGVE))
    Educational VR may increase engagement and retention compared to traditional learning, for some topics or students. However, a student could still get distracted and disengaged due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting distraction. For example, we previously considered gaze visualizations to help teachers understand student attention to better identify or guide distracted students. However, it is not practical for a teacher to monitor a large numbers of student indicators while teaching. To help filter students based on distraction level, we consider a deep learning approach to detect distraction from gaze data. The key aspects are: (1) we created a labeled eye gaze dataset (3.4M data points) from an educational VR environment, (2) we propose an automatic system to gauge a student's distraction level from gaze data, and (3) we apply and compare three deep neural classifiers for this purpose. A proposed CNN-LSTM classifier achieved an accuracy of 89.8\% for classifying distraction, per educational activity section, into one of three levels. 
    more » « less
  5. ; ; ( , Virtual Worlds)
    Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email