# TinyGaze: Lightweight Gaze-Gesture Recognition on Commodity Mobile Devices

arXiv: https://arxiv.org/abs/2604.09658 · HTML: https://mlsanity.com/paper/2604.09658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Human Activity Recognition from Wearable Sensor Data Using Self-Attention](https://mlsanity.com/paper/2003.09018/) (2020)
- [Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues](https://mlsanity.com/paper/1805.03064/) (2018)
- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [Quantifying the Impact of Motion on 2D Gaze Estimation in Real-World Mobile Interactions](https://mlsanity.com/paper/2502.10570/) (2025)
- [DynamicRead: Exploring Robust Gaze Interaction Methods for Reading on Handheld Mobile Devices under Dynamic Conditions](https://mlsanity.com/paper/2304.09584/) (2023)
- [HOIGaze: Gaze Estimation During Hand-Object Interactions in Extended Reality Exploiting Eye-Hand-Head Coordination](https://mlsanity.com/paper/2504.19828/) (2025)
- [MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking](https://mlsanity.com/paper/2505.22769/) (2025)
- [Identity Deepfake Threats to Biometric Authentication Systems: Public and Expert Perspectives](https://mlsanity.com/paper/2506.06825/) (2025)
- [The People's Gaze: Co-Designing and Refining Gaze Gestures with General Users and Gaze Interaction Experts](https://mlsanity.com/paper/2603.05513/) (2026)

## Built on by (most cited first, 1 of 1)

- [Factor-Informed Uncertainty Distillation for Gaze Estimation](https://mlsanity.com/paper/2607.20072/) (2026)

## Abstract (arXiv)

Gaze gestures can provide hands free input on mobile devices, but practical use requires (i) gestures users can learn and recall and (ii) recognition models that are efficient enough for on-device deployment. We present an end-to-end pipeline using commodity ARKit head/eye transforms and a scaffolded guidance-to-recall protocol grounded in learning theory. In a pilot feasibility study (N=4 participants; 240 trials; controlled single-session setting), we benchmark a compact time-series model (TinyHAR) against deeper baselines (DeepConvLSTM, SA-HAR) on 5-way gesture recognition and 4-way user identification. TinyHAR achieves strong performance in this pilot benchmark (Macro F1 = 0.960 for gesture recognition; Macro F1 = 0.997 for user identification) while using only 46k parameters. A modality analysis further indicates that head pose dynamics are highly informative for mobile gaze gestures, highlighting embodied head--eye coordination as a key design consideration. Although the small sample size and controlled setting limit generalizability, these results indicate a potential direction for further investigation into on-device gaze gesture recognition.