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Gaze estimation, which involves perceiving human gaze directions, is the foundation of gaze analysis. It provides crucial clues for understanding human attention and intention. However, most existing methods are designed for constrained ...

DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming increasingly deceptive. Prior research has seen numerous attempts by ...

Gaze estimation plays a crucial role in interactive applications. Recent advancements in deep learning have significantly enhanced appearance-based methods. However, existing approaches often focus on one eye and do not consider binocular gaze, ...$\mathrm{}$

Gaze refers to the directed focus of an individual’s visual perception, playing a fundamental role in human communication and cognition. Recent studies have employed neural networks to predict gaze from standard RGB face images. However, obtaining ...$\mathrm{}$$\mathrm{}$

Existing deep learning-based gaze estimation methods achieved high accuracy, and the prerequisite for ensuring their performance is large-scale datasets with gaze labels. However, collecting large-scale gaze datasets is time-consuming and ...

Eye gaze provides valuable cues about human intent, making gaze estimation a hot topic. Extracting multi-scale information has recently proven effective for gaze estimation in complex scenarios. However, existing methods for estimating gaze based ...

• We propose a network for gaze estimation with pyramidal attention and consistency.
• Pyramidal Perceptual Attention Module extracts multi-scale spatial features.
• Hierarchical Consistency Constraint enhances gaze semantic consistency.

Prior works have addressed the problem of driver intention prediction (DIP) by identifying maneuvers after their onset. On the other hand, early anticipation is equally important in scenarios that demand a preemptive response before a maneuver ...

Deep-learning based gaze estimation methods suffer from sever performance degradation in cross-domain settings. One of the primary reason is that the gaze estimation model is confounded by gaze-irrelevant factor during estimation, such as identity ...

Recently, plain vision transformers (ViTs) have shown impressive performance in various computer vision tasks due to their powerful modeling capabilities and large-scale pre-training. However, they have yet to show excellent results in gaze ...

Human gaze is a crucial cue used in various applications such as human-robot interaction, autonomous driving, and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze angels. However, ...

Recognition and understanding of facial images or eye images are critical for eye tracking. Recent studies have shown that the simultaneous use of facial and eye images can effectively lower gaze errors. However, these methods typically consider ...

• Gaze estimation with feature decomposition as the core concept.
• Correction of gaze estimation by purifying head pose.
• Leverage the relationship between facial and eye images at the feature level.
• Decompose features through ...

Driver gaze plays a key role in different gaze-based applications, such as driver attentiveness detection, visual distraction detection, gaze behavior understanding, and building driver assistance system. The main objective of this study is to ...

• Comprehensive summary of driver gaze fundamentals and bench mark datasets.
• Discussion of driver gaze estimation algorithms.
• Review of driver gaze behavior understanding at different road section.

Self-supervised learning has been a powerful training paradigm to facilitate representation learning. In this study, we design a masked autoencoder (MAE) to guide deep learning models to learn electroencephalography (EEG) signal representation. ...

Calibrating gaze estimation models is crucial to maximize the effectiveness of these systems, although its implementation also poses challenges related to usability. Therefore, the simplification of this process is key. In this work, we dissect the ...

Gaze-annotated facial data is crucial for training deep neural networks (DNNs) for gaze estimation. However, obtaining these data is labor-intensive and requires specialized equipment due to the challenge of accurately annotating the gaze direction of a ...

Gaze estimation aims to predict accurate gaze direction from natural eye images, which is an extreme challenging task due to both random variations in head pose and person-specific biases. Existing works often independently learn features from ...

Advances in face swapping have enabled the automatic generation of highly realistic faces. Yet face swaps are perceived differently than when looking at real faces, with key differences in viewer behavior surrounding the eyes. Face swapping ...

Display Omitted

• Face swaps are generally perceived as more uncanny than real faces.
• Pretrained gaze estimation models can be used to design targeted gaze loss terms.
• Targeted loss equations decrease gaze angle error of generated face swaps.
• ...

Gaze estimation, which has a wide range of applications in many scenarios, is a challenging task due to various unconstrained conditions. As information from both full-face and eye images is instrumental in improving gaze estimation, many ...

The changes in gaze are often reflected in the movements of eye landmarks, highlighting the relevance of eye landmark learning for accurate gaze estimation. To leverage eye landmarks, we propose a gaze estimation framework that incorporates eye ...

• We leverage eye landmark detection as an auxiliary task to assist gaze estimation.
• We apply semi-supervised learning to learn eye landmarks on the real-world data.
• The gaze estimation performance is improved by leveraging eye ...

Eye gaze serves as a primary indicator in identifying the human interest and attention for human-computer interaction (HCI) studies. Most of the eye tracking technologies uses either specialized hardware or requires large number of calibration images to ...