40 research outputs found
Low-cost Geometry-based Eye Gaze Detection using Facial Landmarks Generated through Deep Learning
Introduction: In the realm of human-computer interaction and behavioral
research, accurate real-time gaze estimation is critical. Traditional methods
often rely on expensive equipment or large datasets, which are impractical in
many scenarios. This paper introduces a novel, geometry-based approach to
address these challenges, utilizing consumer-grade hardware for broader
applicability. Methods: We leverage novel face landmark detection neural
networks capable of fast inference on consumer-grade chips to generate accurate
and stable 3D landmarks of the face and iris. From these, we derive a small set
of geometry-based descriptors, forming an 8-dimensional manifold representing
the eye and head movements. These descriptors are then used to formulate linear
equations for predicting eye-gaze direction. Results: Our approach demonstrates
the ability to predict gaze with an angular error of less than 1.9 degrees,
rivaling state-of-the-art systems while operating in real-time and requiring
negligible computational resources. Conclusion: The developed method marks a
significant step forward in gaze estimation technology, offering a highly
accurate, efficient, and accessible alternative to traditional systems. It
opens up new possibilities for real-time applications in diverse fields, from
gaming to psychological research
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world
settings, but existing datasets have been collected under controlled laboratory
conditions and methods have been not evaluated across multiple datasets. In
this work we study appearance-based gaze estimation in the wild. We present the
MPIIGaze dataset that contains 213,659 images we collected from 15 participants
during natural everyday laptop use over more than three months. Our dataset is
significantly more variable than existing ones with respect to appearance and
illumination. We also present a method for in-the-wild appearance-based gaze
estimation using multimodal convolutional neural networks that significantly
outperforms state-of-the art methods in the most challenging cross-dataset
evaluation. We present an extensive evaluation of several state-of-the-art
image-based gaze estimation algorithms on three current datasets, including our
own. This evaluation provides clear insights and allows us to identify key
research challenges of gaze estimation in the wild
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 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
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly
from a single face or eye image. However, due to important variabilities in eye
shapes and inner eye structures amongst individuals, universal models obtain
limited accuracies and their output usually exhibit high variance as well as
biases which are subject dependent. Therefore, increasing accuracy is usually
done through calibration, allowing gaze predictions for a subject to be mapped
to his/her actual gaze. In this paper, we introduce a novel image differential
method for gaze estimation. We propose to directly train a differential
convolutional neural network to predict the gaze differences between two eye
input images of the same subject. Then, given a set of subject specific
calibration images, we can use the inferred differences to predict the gaze
direction of a novel eye sample. The assumption is that by allowing the
comparison between two eye images, annoyance factors (alignment, eyelid
closing, illumination perturbations) which usually plague single image
prediction methods can be much reduced, allowing better prediction altogether.
Experiments on 3 public datasets validate our approach which constantly
outperforms state-of-the-art methods even when using only one calibration
sample or when the latter methods are followed by subject specific gaze
adaptation.Comment: Extension to our paper A differential approach for gaze estimation
with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by
PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Cursor control by point-of-regard estimation for a computer with integrated webcam
This work forms part of the project Eye-Communicate funded by the Malta Council for Science and Technology through the National Research & Innovation Programme (2012) under Research Grant No. R&I-2012-057.The problem of eye-gaze tracking by videooculography has been receiving extensive interest throughout the years owing to the wide range of applications associated with this technology. Nonetheless, the emergence of a new paradigm referred to as pervasive eye-gaze tracking, introduces new challenges that go beyond the typical conditions for which classical video-based eye- gaze tracking methods have been developed. In this paper, we propose to deal with the problem of point-of-regard estimation from low-quality images acquired by an integrated camera inside a notebook computer. The proposed method detects the iris region from low-resolution eye region images by its intensity values rather than the shape, ensuring that this region can also be detected at different angles of rotation and under partial occlusion by the eyelids. Following the calculation of the point- of-regard from the estimated iris center coordinates, a number of Kalman filters improve upon the noisy point-of-regard estimates to smoothen the trajectory of the mouse cursor on the monitor screen. Quantitative results obtained from a validation procedure reveal a low mean error that is within the footprint of the average on-screen icon.peer-reviewe
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