Gaze Estimation
73 papers with code • 9 benchmarks • 16 datasets
Gaze Estimation is a task to predict where a person is looking at given the person’s full face. The task contains two directions: 3-D gaze vector and 2-D gaze position estimation. 3-D gaze vector estimation is to predict the gaze vector, which is usually used in the automotive safety. 2-D gaze position estimation is to predict the horizontal and vertical coordinates on a 2-D screen, which allows utilizing gaze point to control a cursor for human-machine interaction.
Source: A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
Benchmarks
These leaderboards are used to track progress in Gaze Estimation
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AVA
MPIIGaze
Gaze360
GazeCapture
EYEDIAP
ETH-XGaze
OpenEDS
Latest papers
TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation
pbonazzi/retina
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We propose TinyTracker, a highly efficient, fully quantized model for 2D gaze estimation designed to maximize the performance of the edge vision systems considered in this study.
Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation
This work proposes a generalizable multi-view gaze estimation task and a cross-view feature fusion method to address this issue.
Precise localization of corneal reflections in eye images using deep learning trained on synthetic data
Our method outperformed state-of-the-art algorithmic methods on real eye images with a 35% reduction in terms of spatial precision, and performed on par with state-of-the-art on simulated images in terms of spatial accuracy. We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem which is one of the important common roadblocks in the development of deep learning models for gaze estimation.
Towards Precision in Appearance-based Gaze Estimation in the Wild
Appearance-based gaze estimation systems have shown great progress recently, yet the performance of these techniques depend on the datasets used for training.
Using Gaze for Behavioural Biometrics
A principled approach to the analysis of eye movements for behavioural biometrics is laid down.
Source-Free Adaptive Gaze Estimation by Uncertainty Reduction
caixin1998/unrega
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CVPR 2023
In light of this, we present an unsupervised source-free domain adaptation approach for gaze estimation, which adapts a source-trained gaze estimator to unlabeled target domains without source data.
3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
vagver/3dgazenet
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To close the gap between image domains, we create a large-scale dataset of diverse faces with gaze pseudo-annotations, which we extract based on the 3D geometry of the scene, and design a multi-view supervision framework to balance their effect during training.
One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with Single Encoders
rishipython/one-eye-is-all-you-need-lightweight-ensembles-for-gaze-estimation-with-single-encoders
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Thus, we propose a gaze estimation model that implements the ResNet and Inception model architectures and makes predictions using only one eye image.
Contrastive Representation Learning for Gaze Estimation
jswati31/gazeclr
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Notably, SSL exploits contrastive learning to encourage visual representations to be invariant under various image transformations.
LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation
Although recent gaze estimation methods lay great emphasis on attentively extracting gaze-relevant features from facial or eye images, how to define features that include gaze-relevant components has been ambiguous.
