Predicting Gaze Location without Camera or Eye-Tracker

Saman Rezapoor; Sajad Shirali-Shahreza; Gerald Penn

doi:10.63317/2hpa22f63k2t

Predicting Gaze Location without Camera or Eye-Tracker

Saman Rezapoor, Sajad Shirali-Shahreza, Gerald Penn

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Abstract

The task of identifying the location that a user looks at, commonly known as gaze estimation, has various HCI and NLP applications. Traditional gaze estimation methods use special hardware such as eye-trackers or ordinary cameras such as webcams to perform this. However, they are not applicable to the majority of web users either because the user does not have them or does not want to use them due to privacy reasons. In this paper, we propose the idea of using multimodal LLMs to analyze the content of the user’s screen along with mouse location to estimate the gaze location. It primarily uses the results of studies that extract common reading patterns such as the F-pattern and Z-pattern. Our experimental results on The Eye Of The Typer (EOTT) dataset provide promising results for estimating gaze location.

Anthology ID:: 2026.gaze4nlp-1.9
Volume:: Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Cengiz Acartürk, Burcu Can, Jamal Nasir, Çağrı Çöltekin
Venues:: Gaze4NLP | WS
SIG:
Publisher:: ELDA
Note:
Pages:: 58–63
Language:
External URL:: https://lrec.elra.info/lrec2026-ws-gaze4nlp-09
DOI:: 10.63317/2hpa22f63k2t
Bibkey:
Cite (ACL):: Saman Rezapoor, Sajad Shirali-Shahreza, and Gerald Penn. 2026. Predicting Gaze Location without Camera or Eye-Tracker. In Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP, pages 58–63, Palma de Mallorca, Spain. ELDA.
Cite (Informal):: Predicting Gaze Location without Camera or Eye-Tracker (Rezapoor et al., Gaze4NLP 2026)
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@inproceedings{rezapoor-etal-2026-predicting,
    title = "Predicting Gaze Location without Camera or Eye-Tracker",
    author = "Rezapoor, Saman  and
      Shirali-Shahreza, Sajad  and
      Penn, Gerald",
    editor = {Acart{\"u}rk, Cengiz  and
      Can, Burcu  and
      Nasir, Jamal  and
      {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
    booktitle = "Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned {NLP}",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELDA",
    url = "https://aclanthology.org/2026.gaze4nlp-1.9/",
    doi = "10.63317/2hpa22f63k2t",
    pages = "58--63",
    abstract = "The task of identifying the location that a user looks at, commonly known as gaze estimation, has various HCI and NLP applications. Traditional gaze estimation methods use special hardware such as eye-trackers or ordinary cameras such as webcams to perform this. However, they are not applicable to the majority of web users either because the user does not have them or does not want to use them due to privacy reasons. In this paper, we propose the idea of using multimodal LLMs to analyze the content of the user{'}s screen along with mouse location to estimate the gaze location. It primarily uses the results of studies that extract common reading patterns such as the F-pattern and Z-pattern. Our experimental results on The Eye Of The Typer (EOTT) dataset provide promising results for estimating gaze location."
}

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%0 Conference Proceedings
%T Predicting Gaze Location without Camera or Eye-Tracker
%A Rezapoor, Saman
%A Shirali-Shahreza, Sajad
%A Penn, Gerald
%Y Acartürk, Cengiz
%Y Can, Burcu
%Y Nasir, Jamal
%Y Çöltekin, Çağrı
%S Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP
%D 2026
%8 May
%I ELDA
%C Palma de Mallorca, Spain
%F rezapoor-etal-2026-predicting
%X The task of identifying the location that a user looks at, commonly known as gaze estimation, has various HCI and NLP applications. Traditional gaze estimation methods use special hardware such as eye-trackers or ordinary cameras such as webcams to perform this. However, they are not applicable to the majority of web users either because the user does not have them or does not want to use them due to privacy reasons. In this paper, we propose the idea of using multimodal LLMs to analyze the content of the user’s screen along with mouse location to estimate the gaze location. It primarily uses the results of studies that extract common reading patterns such as the F-pattern and Z-pattern. Our experimental results on The Eye Of The Typer (EOTT) dataset provide promising results for estimating gaze location.
%R 10.63317/2hpa22f63k2t
%U https://aclanthology.org/2026.gaze4nlp-1.9/
%U https://doi.org/10.63317/2hpa22f63k2t
%P 58-63

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Markdown (Informal)

[Predicting Gaze Location without Camera or Eye-Tracker](https://aclanthology.org/2026.gaze4nlp-1.9/) (Rezapoor et al., Gaze4NLP 2026)

Predicting Gaze Location without Camera or Eye-Tracker (Rezapoor et al., Gaze4NLP 2026)

ACL

Saman Rezapoor, Sajad Shirali-Shahreza, and Gerald Penn. 2026. Predicting Gaze Location without Camera or Eye-Tracker. In Proceedings fo the Second International Workshop on Eye-Tracking Resources and Evaluation for Human-Aligned NLP, pages 58–63, Palma de Mallorca, Spain. ELDA.