GazeCode: Recall-Based Verification for Higher-Quality In-the-Wild Mobile Gaze Data Collection
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910120314667008 |
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| author | Lei, Yaxiong Davies, Thomas Gong, Xinya He, Shijing Ye, Juan |
| author_facet | Lei, Yaxiong Davies, Thomas Gong, Xinya He, Shijing Ye, Juan |
| contents | Large-scale mobile gaze estimation relies on in-the-wild datasets, yet unsupervised collection makes it difficult to verify whether participants truly foveate logged targets. Prior mobile protocols often use low-entropy validation (e.g., binary probes) that can be satisfied by guessing and may still allow peripheral viewing, introducing label noise. We present \textbf{GazeCode}, a recall-based verification paradigm for higher-confidence in-the-wild mobile gaze data collection that strengthens \emph{label validity} through a multi-digit recall task (reducing random success to $10^{-N}$) paired with anti-peripheral stimulus design (small, low-contrast, brief digits). The system logs synchronized front-camera video, IMU streams, and target events using high-resolution timestamps. In a formative study (N=3), we probe key parameters (opacity, duration) and directly test peripheral exploitability using an eccentricity-controlled \textit{RING} condition. Results show that low-opacity digits substantially reduce peripheral readability while remaining usable for attentive foveation, supporting the inference that correct recall corresponds to higher-confidence gaze labels. We conclude with actionable design guidelines for robust in-the-wild gaze data collection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09659 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GazeCode: Recall-Based Verification for Higher-Quality In-the-Wild Mobile Gaze Data Collection Lei, Yaxiong Davies, Thomas Gong, Xinya He, Shijing Ye, Juan Human-Computer Interaction H.5 Large-scale mobile gaze estimation relies on in-the-wild datasets, yet unsupervised collection makes it difficult to verify whether participants truly foveate logged targets. Prior mobile protocols often use low-entropy validation (e.g., binary probes) that can be satisfied by guessing and may still allow peripheral viewing, introducing label noise. We present \textbf{GazeCode}, a recall-based verification paradigm for higher-confidence in-the-wild mobile gaze data collection that strengthens \emph{label validity} through a multi-digit recall task (reducing random success to $10^{-N}$) paired with anti-peripheral stimulus design (small, low-contrast, brief digits). The system logs synchronized front-camera video, IMU streams, and target events using high-resolution timestamps. In a formative study (N=3), we probe key parameters (opacity, duration) and directly test peripheral exploitability using an eccentricity-controlled \textit{RING} condition. Results show that low-opacity digits substantially reduce peripheral readability while remaining usable for attentive foveation, supporting the inference that correct recall corresponds to higher-confidence gaze labels. We conclude with actionable design guidelines for robust in-the-wild gaze data collection. |
| title | GazeCode: Recall-Based Verification for Higher-Quality In-the-Wild Mobile Gaze Data Collection |
| topic | Human-Computer Interaction H.5 |
| url | https://arxiv.org/abs/2604.09659 |