Gaze Estimation: A machine learning approach to determine where a person is looking.
Gaze estimation is an important aspect of computer vision, human-computer interaction, and robotics, as it provides insights into human attention and intention. With the advent of deep learning, significant advancements have been made in the field of gaze estimation, leading to more accurate and efficient systems. However, challenges remain in terms of computational cost, reliance on large-scale labeled data, and performance degradation when applied to new domains.
Recent research in gaze estimation has focused on various aspects, such as local network sharing, multitask learning, unsupervised gaze representation learning, and domain adaptation. For instance, the LNSMM method estimates eye gaze points and directions simultaneously using a local sharing network and a Multiview Multitask Learning framework. On the other hand, FreeGaze is a resource-efficient framework that incorporates frequency domain gaze estimation and contrastive gaze representation learning to overcome the limitations of existing supervised learning-based solutions.
Another approach, called LatentGaze, selectively utilizes gaze-relevant features in a latent code through gaze-aware analytic manipulation, improving cross-domain gaze estimation accuracy. Additionally, ETH-XGaze is a large-scale dataset that aims to improve the robustness of gaze estimation methods across different head poses and gaze angles, providing a standardized experimental protocol and evaluation metric for future research.
Practical applications of gaze estimation include attention-aware mobile systems, cognitive psychology research, and human-computer interaction. For example, a company could use gaze estimation to improve the user experience of their products by understanding where users are looking and adapting the interface accordingly. Another application could be in the field of robotics, where robots could use gaze estimation to better understand human intentions and interact more effectively.
In conclusion, gaze estimation is a crucial aspect of understanding human attention and intention, with numerous applications across various fields. While deep learning has significantly improved the accuracy and efficiency of gaze estimation systems, challenges remain in terms of computational cost, data requirements, and domain adaptation. By addressing these challenges and building upon recent research, gaze estimation can continue to advance and contribute to a deeper understanding of human behavior and interaction.
Gaze Estimation Further Reading1.LNSMM: Eye Gaze Estimation With Local Network Share Multiview Multitask http://arxiv.org/abs/2101.07116v1 Yong Huang, Ben Chen, Daiming Qu2.FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning http://arxiv.org/abs/2209.06692v1 Lingyu Du, Guohao Lan3.Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis http://arxiv.org/abs/1904.10638v1 Yu Yu, Gang Liu, Jean-Marc Odobez4.LatentGaze: Cross-Domain Gaze Estimation through Gaze-Aware Analytic Latent Code Manipulation http://arxiv.org/abs/2209.10171v1 Isack Lee, Jun-Seok Yun, Hee Hyeon Kim, Youngju Na, Seok Bong Yoo5.Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze http://arxiv.org/abs/2010.07811v2 Bardia Doosti, Ching-Hui Chen, Raviteja Vemulapalli, Xuhui Jia, Yukun Zhu, Bradley Green6.ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation http://arxiv.org/abs/2007.15837v1 Xucong Zhang, Seonwook Park, Thabo Beeler, Derek Bradley, Siyu Tang, Otmar Hilliges7.Jitter Does Matter: Adapting Gaze Estimation to New Domains http://arxiv.org/abs/2210.02082v1 Ruicong Liu, Yiwei Bao, Mingjie Xu, Haofei Wang, Yunfei Liu, Feng Lu8.Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark http://arxiv.org/abs/2104.12668v1 Yihua Cheng, Haofei Wang, Yiwei Bao, Feng Lu9.Offset Calibration for Appearance-Based Gaze Estimation via Gaze Decomposition http://arxiv.org/abs/1905.04451v2 Zhaokang Chen, Bertram E. Shi10.Vulnerability of Appearance-based Gaze Estimation http://arxiv.org/abs/2103.13134v1 Mingjie Xu, Haofei Wang, Yunfei Liu, Feng Lu
Gaze Estimation Frequently Asked Questions
What is gaze estimation?
Gaze estimation is a machine learning approach used to determine where a person is looking. It is an essential aspect of computer vision, human-computer interaction, and robotics, as it provides insights into human attention and intention. By analyzing eye movements and positions, gaze estimation systems can predict the point of focus or gaze direction of an individual.
How is gaze measured?
Gaze is typically measured by tracking the position and movement of the eyes, along with the head pose. Various techniques can be used for gaze measurement, such as video-based eye tracking, infrared-based tracking, and electrooculography (EOG). In recent years, deep learning methods have been employed to improve the accuracy and efficiency of gaze estimation systems.
How does gaze tracking work?
Gaze tracking works by capturing and analyzing eye movements and positions to determine where a person is looking. It usually involves the use of cameras, infrared sensors, or other tracking devices to monitor the eyes and head pose. Machine learning algorithms, particularly deep learning models, are then used to process the captured data and estimate the gaze direction or point of focus.
What is gaze in vision?
In the context of vision, gaze refers to the direction in which a person is looking or focusing their attention. It is an essential aspect of human perception and interaction, as it provides insights into an individual's attention, intention, and cognitive processes. Gaze estimation techniques aim to determine this gaze direction or point of focus by analyzing eye movements and positions.
What are the challenges in gaze estimation?
The main challenges in gaze estimation include computational cost, reliance on large-scale labeled data, and performance degradation when applied to new domains. Developing accurate and efficient gaze estimation systems requires significant computational resources and large amounts of labeled data for training. Additionally, the performance of these systems may degrade when applied to new domains or environments, necessitating domain adaptation techniques.
What are some recent advancements in gaze estimation research?
Recent advancements in gaze estimation research include local network sharing, multitask learning, unsupervised gaze representation learning, and domain adaptation. Methods such as LNSMM and FreeGaze have been developed to improve the accuracy and efficiency of gaze estimation systems. Additionally, approaches like LatentGaze and the ETH-XGaze dataset aim to address the challenges of domain adaptation and robustness across different head poses and gaze angles.
What are some practical applications of gaze estimation?
Practical applications of gaze estimation include attention-aware mobile systems, cognitive psychology research, human-computer interaction, and robotics. For example, companies can use gaze estimation to improve user experience by understanding where users are looking and adapting interfaces accordingly. In robotics, gaze estimation can help robots better understand human intentions and interact more effectively. Additionally, gaze estimation can be used in cognitive psychology research to study attention, perception, and other cognitive processes.
How can gaze estimation improve human-computer interaction?
Gaze estimation can improve human-computer interaction by providing insights into user attention and intention. By understanding where users are looking, systems can adapt interfaces, content, and interactions to better suit individual needs and preferences. This can lead to more intuitive, efficient, and personalized user experiences, ultimately enhancing the overall effectiveness of human-computer interaction.
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