arXiv subjects

Yasmeen Abdrabou

Publications and source records attributed to Yasmeen Abdrabou.

12 recordsLinked to original sources

arXiv2026

An Affordable, Wearable Stereo-Eye-Tracking Platform

Research on video-based eye-tracking has long explored stereo and glint-based methods, yet existing wearable eye trackers - both commercial and open-source - offer limited flexibility for algorithm development and comparative evaluation. We present an affordable, wearable stereo eye-tracking platform built from off-the-shelf and 3D-printable components that explicitly targets this gap. The system combines four infrared eye cameras, infrared illumination, an optional scene camera, and software support for calibration and synchronized data acquisition. By design, the platform supports multiple eye-tracking paradigms, including stereo, glint-based, and binocular approaches, within a single hardware configuration. Rather than optimizing for end-user robustness, the platform prioritizes modularity and extensibility for research use. This paper focuses on the hardware architecture and calibration pipeline and demonstrates the feasibility of the approach using a prototype implementation. All hardware designs and documentation are made openly available.

cs.CV↗

arXiv2026

VIVA Stimuli: A Web-Based Platform for Eye Tracking Stimuli

Reproducibility in eye-tracking research is increasingly important as researchers conduct diverse experiments and seek to validate or replicate findings. However, exact replication remains challenging due to differences in laboratory practices and experimental setups. Inconsistent stimulus presentation can yield divergent metrics from identical oculomotor behavior, yet the stimulus layer remains largely unstandardized. Existing tools often require programming expertise or depend on specific hardware vendors. We introduce VIVA Stimuli, a web-based platform for standardized eye-tracking stimulus presentation. It provides configurable task types, including fixation, smooth pursuit, cognitive load, blink, slippage, content display, and questionnaires within a unified environment. The platform supports any eye-tracking technology, including wearable and screen-based VOG trackers, LFI sensors, and EOG devices. ArUco markers enable synchronization for trackers with scene cameras, while a WebSocket architecture ensures temporal synchronization for those without. A visual experiment flow editor allows protocols to be exported and shared, enabling identical stimulus replication across laboratories.

cs.HC↗

arXiv2026

Secure Storage and Privacy-Preserving Scanpath Comparison via Garbled Circuits in Eye Tracking

With the growing use of eye tracking on VR and mobile platforms, gaze data is increasing. While scanpath comparison is important to gaze behavior analysis, existing methods lack privacy-preserving capabilities for real-world use. We present a garbled-circuit (GC)-based approach enabling secure storage and privacy-preserving scanpath comparison under the semi-honest model. It supports two configurations: (1) a two-party setting where the data owner and processor jointly compute similarity scores without revealing their inputs, and (2) a server-assisted setting where encrypted scanpaths are stored and processed while the data owner remains offline. All decryption and comparison operations are executed inside the GC. Experiments on three eye-tracking datasets evaluate fidelity, runtime, and communication, and show secure results for MultiMatch, ScanMatch, and SubsMatch closely match plaintext outcomes, with manageable runtime and communication overhead. Tests under various network conditions indicate that the design remains feasible for real-world privacy-preserving scanpath analysis and can be extended to other GC-based behavioral algorithms.

cs.CR↗

arXiv2026

Lazy or Efficient? Towards Accessible Eye-Tracking Event Detection Using LLMs

Gaze event detection is fundamental to vision science, human-computer interaction, and applied analytics. However, current workflows often require specialized programming knowledge and careful handling of heterogeneous raw data formats. Classical detectors such as I-VT and I-DT are effective but highly sensitive to preprocessing and parameterization, limiting their usability outside specialized laboratories. This work introduces a code-free, large language model (LLM)-driven pipeline that converts natural language instructions into an end-to-end analysis. The system (1) inspects raw eye-tracking files to infer structure and metadata, (2) generates executable routines for data cleaning and detector implementation from concise user prompts, (3) applies the generated detector to label fixations and saccades, and (4) returns results and explanatory reports, and allows users to iteratively optimize their code by editing the prompt. Evaluated on public benchmarks, the approach achieves accuracy comparable to traditional methods while substantially reducing technical overhead. The framework lowers barriers to entry for eye-tracking research, providing a flexible and accessible alternative to code-intensive workflows.

cs.HC↗

arXiv2026

As Far as Eye See: Vergence-Pupil Coupling in Near-Far Depth Switching

Vergence is widely used as a proxy for depth perception and spatial attention in immersive and real-world eye-tracking studies. In this paper, we investigate how pupil size artefacts affect vergence estimates during real physical depth viewing with a head-mounted eye tracker. Using a beamsplitter setup with physically near and far targets, we elicited controlled convergent and divergent eye movements under static, luminance-modulated, and blockwise fixation conditions. Near and far targets were reliably separable in vergence angle across participants. However, pupil-vergence coupling varied substantially across individuals and conditions. Static illumination produced large inter-participant variability, while luminance modulation reduced this spread, yielding more clustered estimates. Blockwise and audio-cued recordings further showed that pupil-vergence coupling persists even without visual depth onsets. These results suggest that pupil size fluctuations can systematically influence vergence estimates, and that controlled viewing conditions can reduce--but not eliminate--this effect.

cs.HC↗

arXiv2026

Night Eyes: A Reproducible Framework for Constellation-Based Corneal Reflection Matching

Corneal reflection (glint) detection plays an important role in pupil-corneal reflection (P-CR) eye tracking, but in practice it is often handled as heuristics embedded within larger systems, making reproducibility difficult across hardware setups. We introduce a 2D geometry-driven, constellation-based pipeline for mulit-glint detection and matching, focusing on reproducibility and clear evaluation. Inspired by lost-in-space star identification, we treat glints as structured constellations rather than independent blobs. We propose a Similarity-Layout Alignment (SLA) procedure which adapts constellation matching to the specific constraints of multi-LED eye tracking. The framework brings together controlled over-detection, adaptive candidate fallback, appearance-aware scoring, and optional semantic layout priors while keeping detection and correspondence explicitly separated. Evaluated on a public multi-LED dataset, the system provides stable identity-preserving correspondence under noisy conditions. We release code, presets, and evaluation scripts to enable transparent replication, comparison, and dataset annotation.

cs.CV↗

arXiv2026

GazeBlend: Exploring Paired Gaze-Based Input Techniques for Navigation and Selection Tasks on Mobile Devices

The potential of gaze for hands-free mobile interaction is increasingly evident. While each gaze input technique presents distinct advantages and limitations, a combination can amplify strengths and mitigate challenges. We report on the results of a user study (N=24), in which we compared the usability and performance of pairing three popular gaze input techniques: Dwell Time, Pursuits, and Gaze Gestures, for navigation and selection tasks while sitting and walking. Results show that pairing gestures for navigation with either Dwell time or Pursuits for selection improves task completion time and rate compared to using either individually. We discuss the implications of pairing gaze input techniques, such as how Pursuits may negatively impact other techniques, likely due to the visual clutter it adds, how integrating gestures for navigation reduces the chances of unintentional selections, and the impact of motor activity on performance. Our findings provide insights for effective gaze-enabled interfaces.

cs.HC↗

arXiv2025

Mind the Gaze: Improving the Usability of Dwell Input by Adapting Gaze Targets Based on Viewing Distance

Dwell input shows promise for handheld mobile contexts, but its performance is impacted by target size and viewing distance. While fixed target sizes suffice in static setups, in mobile settings, frequent posture changes alter viewing distances, which in turn distort perceived size and hinder dwell performance. We address this through GAUI, a Gaze-based Adaptive User Interface that dynamically resizes targets to maximise performance at the given viewing distance. In a two-phased study (N=24), GAUI leveraged the strengths of its distance-responsive design, outperforming the large UI static baseline in task time, and being less error-prone than the small UI static baseline. It was rated the most preferred interface overall. Participants reflected on using GAUI in six different postures. We discuss how their experience is impacted by posture, and propose guidelines for designing context-aware adaptive UIs for dwell interfaces on handheld mobile devices that maximise performance.

cs.HC↗

arXiv2025

Investigating the Effect of Encumbrance on Gaze- and Touch-based Target Acquisition on Handheld Mobile Devices

The potential of using gaze as an input modality in the mobile context is growing. While users often encumber themselves by carrying objects and using mobile devices while walking, the impact of encumbrance on gaze input performance remains unexplored. To investigate this, we conducted a user study (N=24) to evaluate the effect of encumbrance on the performance of 1) Gaze using Dwell time (with/without visual feedback), 2) GazeTouch (with/without visual feedback), and 3) One- or two-hand touch input. While Touch generally performed better, Gaze, especially with feedback, showed a consistent performance regardless of whether participants were encumbered or unencumbered. Participants' preferences for input modalities varied with encumbrance: they preferred Gaze when encumbered, and touch when unencumbered. Our findings enhance understanding of the effect of encumbrance on gaze input and contribute towards selecting appropriate input modalities in future mobile user interfaces to account for situational impairments.

cs.HC↗

arXiv2025

Multimodal Behavioral Patterns Analysis with Eye-Tracking and LLM-Based Reasoning

Eye-tracking data reveals valuable insights into users' cognitive states but is difficult to analyze due to its structured, non-linguistic nature. While large language models (LLMs) excel at reasoning over text, they struggle with temporal and numerical data. This paper presents a multimodal human-AI collaborative framework designed to enhance cognitive pattern extraction from eye-tracking signals. The framework includes: (1) a multi-stage pipeline using horizontal and vertical segmentation alongside LLM reasoning to uncover latent gaze patterns; (2) an Expert-Model Co-Scoring Module that integrates expert judgment with LLM output to generate trust scores for behavioral interpretations; and (3) a hybrid anomaly detection module combining LSTM-based temporal modeling with LLM-driven semantic analysis. Our results across several LLMs and prompt strategies show improvements in consistency, interpretability, and performance, with up to 50% accuracy in difficulty prediction tasks. This approach offers a scalable, interpretable solution for cognitive modeling and has broad potential in adaptive learning, human-computer interaction, and educational analytics.

cs.HC↗

arXiv2025

User Identification with LFI-Based Eye Movement Data Using Time and Frequency Domain Features

Laser interferometry (LFI)-based eye-tracking systems provide an alternative to traditional camera-based solutions, offering improved privacy by eliminating the risk of direct visual identification. However, the high-frequency signals captured by LFI-based trackers may still contain biometric information that enables user identification. This study investigates user identification from raw high-frequency LFI-based eye movement data by analyzing features extracted from both the time and frequency domains. Using velocity and distance measurements without requiring direct gaze data, we develop a multi-class classification model to accurately distinguish between individuals across various activities. Our results demonstrate that even without direct visual cues, eye movement patterns exhibit sufficient uniqueness for user identification, achieving 93.14% accuracy and a 2.52% EER with 5-second windows across both static and dynamic tasks. Additionally, we analyze the impact of sampling rate and window size on model performance, providing insights into the feasibility of LFI-based biometric recognition. Our findings demonstrate the novel potential of LFI-based eye-tracking for user identification, highlighting both its promise for secure authentication and emerging privacy risks. This work paves the way for further research into high-frequency eye movement data.

cs.HC↗

arXiv2023

Revealing the Hidden Effects of Phishing Emails: An Analysis of Eye and Mouse Movements in Email Sorting Tasks

Users are the last line of defense as phishing emails pass filter mechanisms. At the same time, phishing emails are designed so that they are challenging to identify by users. To this end, attackers employ techniques, such as eliciting stress, targeting helpfulness, or exercising authority, due to which users often miss being manipulated out of malicious intent. This work builds on the assumption that manipulation techniques, even if going unnoticed by users, still lead to changes in their behavior. In this work, we present the outcomes of an online study in which we collected gaze and mouse movement data during an email sorting task. Our findings show that phishing emails lead to significant differences across behavioral features but depend on the nature of the email. We discuss how our findings can be leveraged to build security mechanisms protecting users and companies from phishing.

cs.HC↗