arXiv subjects

Oleg V. Komogortsev

Publications and source records attributed to Oleg V. Komogortsev.

At least 19 recordsLinked to original sources

arXiv2026

Measurement-Guided Conversion of Laboratory Eye-Tracking Data for VR Authentication

Eye-movement authentication in virtual reality (VR) needs longitudinal training data, yet most such datasets come from lab trackers. We convert lab recordings to a VR headset's signal quality while keeping their eye movement, using only unpaired VR recordings of other people. Measurements show that the headset's added noise dominates above 7.8 Hz and that its quality is consistent within a person. A parametric device model re-creates the headset's sampling, a person-consistent calibration error and spectrally shaped noise, and a frequency-locked diffusion renderer re-textures that high-frequency noise. Converted GazeBase recordings match GazeBaseVR in noise level and accuracy, keep their source's saccade dynamics, and are harder to tell from real VR data than two prior conversions. An authenticator trained only on converted data verifies unseen VR users at a 5.9% equal error rate with 45 s of gaze, close to real VR training (5.8%); with 5 s, a gap remains.

cs.HC↗

arXiv2026

Privacy-Preserving Gaze Interaction: Reducing Re-Identification Without Degrading Utility

Gaze-based interaction is emerging as a standard input modality on consumer extended-reality (XR) devices. Yet each gaze input constitutes an involuntary biometric disclosure, as the same signal that selects a button can also identify the user who produced it. Reducing identity information without degrading interaction is difficult: most prior methods are validated on small datasets or optimized for only one of the two competing objectives. We introduce a dual-assessment framework that scores any real-time gaze transformation on two axes at once: interaction utility, measured through an offline gaze-interaction simulation and an operational spatial-accuracy metric, and privacy preservation, measured through the Rank-1 Identification Rate (IR) of a state-of-the-art re-identification adversary. We test the framework on 23 conditions: one raw baseline and 22 privacy variants from eight lightweight signal-processing families using two publicly available datasets (GazeBase and GazeBaseVR). Deterministic operators produce no significant change in identity on either dataset, because a subject-invariant mapping preserves the relative geometry a biometric embedding exploits. Identity drops sharply only when identity-uncorrelated randomness is injected per sample. Smoothing applied afterwards to recover signal quality partially restores identifiability on both datasets. The best-balanced configuration reduces the Rank-1 IR by 64.3 percentage points on GazeBase and 67.9 points on GazeBaseVR, while leaving target-selection success essentially unchanged on both datasets. Privacy-preserving gaze interaction is therefore not zero-sum. However, the reduction in identifiability is achieved by injecting randomness rather than by improving signal fidelity.

cs.HC↗

arXiv2026

EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis

Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virtual- and augmented-reality systems, where gaze dynamics contain distinctive subject-specific features. However, robust EMB systems require diverse, high-quality gaze recordings that are expensive to collect and often unavailable at the scale needed for model development. Generative models can mitigate data scarcity, but existing methods either synthesize generic gaze behavior or personalize signals primarily by identity, without jointly representing the user's task and subjective state. Consequently, generated signals may appear visually realistic while failing to retain the behavioral properties required for biometric applications. To address this limitation, we propose EyeMakeYou, a multi-conditional denoising diffusion framework for subject-specific, high-frequency gaze synthesis. EyeMakeYou generates 5-s, 1000-Hz bivariate gaze-velocity sequences from an identity-removed reference trajectory and conditions the denoising process on an identity embedding, a task embedding, and self-reported ratings of overall difficulty, mental tiredness, and eye tiredness. Its objective combines diffusion noise prediction and identity preservation with multi-resolution spectral, drift-consistency, and event-weighted local-smoothness losses. Experiments on GazeBase show that EyeMakeYou achieves higher median spatial accuracy and greater real--synthetic similarity in the embedding feature space than the existing generative approaches, while retaining selected task-dependent associations between subjective reports and oculomotor features. These findings support conditional diffusion as a practical approach for augmenting gaze datasets for biometric and interactive applications.

cs.HC↗

arXiv2026

Quantitative and Qualitative Comparison of Generative Models for Subject-Specific Gaze Synthesis: Diffusion vs GANs

Gaze-based biometrics has emerged as a promising approach for user authentication, but advances in this area are constrained by the limited availability of high-quality, subject-specific gaze recordings. Recent generative models have shown promise for synthesizing gaze data, yet most existing approaches rely on random noise distributions or global, predefined latent embeddings and do not explicitly model subject-specific gaze characteristics. To address this limitation, we revisit two recent generative models, diffusion and generative adversarial networks (GANs), and modify both to support subject-aware gaze synthesis. For the diffusion-based approach, we incorporate compact user embeddings to capture subject-level gaze traits. For the GAN-based approach, we introduce a subject-specific conditioning module that guides the generator to preserve idiosyncratic gaze patterns. Later, we evaluate both approaches using standard eye-movement signal quality metrics, including spatial accuracy and precision, and assess whether the generated sequences retain identity-related features relevant to biometric applications. Experimental results show that the diffusion-based approach produces more realistic, identity-preserving gaze sequences than the GAN-based approach. Overall, this work advances the understanding of synthetic gaze quality, realism, and subject specificity and supports the development of gaze-based biometric applications.

cs.HC↗

arXiv2026

Privatization of Synthetic Gaze: Attenuating State Signatures in Diffusion-Generated Eye Movements

The recent success of deep learning (DL) has enabled the generation of high-quality synthetic data, advancing the development of data-driven biometric applications. Among various biometric modalities, eye movement sequences have emerged as a promising behavioral biometric. However, gaze data also raises privacy concerns because it may encode individuals' internal states, such as fatigue, emotional load, and stress. Ideally, synthetic gaze data should preserve the signal quality of real recordings, including identity features, while removing or attenuating privacy-sensitive, state-related attributes to reduce risks of personal state exposure. Many recent DL-based generative models focus on replicating real gaze trajectories but do not explicitly evaluate whether generated signals retain subjective-state information. In this work, we examine a recent diffusion-based gaze synthesis approach by analyzing the correlations between synthetic gaze features and subjective reports, including fatigue and other self-reported states. Our results show that these correlations are weaker and less stable in synthetic gaze than in real gaze, suggesting the attenuation of state-related signatures under the evaluated protocol. At the same time, synthetic gaze preserves essential signal characteristics similar to real data, supporting its potential use in privacy-aware gaze-based applications.

cs.HC↗

arXiv2026

Towards Robust Iris Recognition Through Occlusion Identification and Conditional Diffusion-Based Reconstruction

Iris recognition is a reliable biometric approach that identifies individuals using the distinctive and stable texture of the iris. However, recognition performance can degrade when discriminative iris texture is partially occluded by eyelids, eyelashes, specular reflections, or other acquisition artifacts. Existing approaches often perform recognition directly on degraded samples or rely only on the remaining visible iris region, which may be inadequate when substantial texture is corrupted. To address this limitation, we propose an occlusion-aware iris recognition framework with three sequential modules: occlusion-type identification, diffusion-based reconstruction, and deep-learning-based recognition. First, a residual 2D CNN-based network determines whether an iris image is non-occluded or belongs to one of the controlled occlusion categories. Second, the occluded image, binary mask, and predicted occlusion type condition a denoising diffusion probabilistic model to reconstruct the corrupted region. Finally, VGG19-HPMNet, a modified VGG19 model with horizontal pyramid mapping, extracts discriminative global and part-wise local iris features for recognition. Experiments on the CASIA-Iris-Thousand dataset under a controlled synthetic-occlusion protocol show that the proposed framework improves iris recognition performance by identifying the occlusion type, reconstructing masked regions, and re-evaluating the restored iris samples.

cs.CV↗

arXiv2026

Enhancing Eye Movement Biometrics for User Authentication via Continuous Gaze Offset Score Fusion

Eye movement biometrics (EMB) use subject-specific gaze dynamics for user authentication and identification. Recent deep learning-based EMB systems achieve strong performance by modeling temporal eye movement behavior. However, these systems typically overlook continuous gaze offset, despite prior evidence that it contains user-discriminative information. This work examines whether continuous gaze offset can improve biometric performance when combined with existing biometric features. We evaluate linear and nonlinear fusion methods on two publicly available datasets, collected via the lab-grade eye tracker and virtual reality headset across multiple tasks and observation durations. Results indicate that fusion offers performance benefits on both datasets, particularly when using nonlinear fusion. Additionally, fusing biometric information across multiple tasks further improves authentication performance. These findings support the hypothesis that continuous gaze offset may serve as useful auxiliary information under conditions of degraded or noisy eye tracking.

cs.HC↗

arXiv2026

Eye Feel You: A DenseNet-driven User State Prediction Approach

Subjective self-reports, collected with eye-tracking data, reveal perceived states like fatigue, effort, and task difficulty. However, these reports are costly to collect and challenging to interpret consistently in longitudinal studies. In this work, we focus on determining whether objective gaze dynamics can reliably predict subjective reports across repeated recording rounds in the eye-tracking dataset. We formulate subjective-report prediction as a supervised regression problem and propose a DenseNet-based deep learning regressor that learns predictive representations from gaze velocity signals. We conduct two complementary experiments to clarify our aims. First, the cross-round generalization experiment tests whether models trained on earlier rounds transfer to later rounds, evaluating the models' ability to capture longitudinal changes. Second, cross-subject generalization tests models' robustness by predicting subjective outcomes for new individuals. These experiments aim to reduce reliance on hand-crafted feature designs and clarify which states of subjective experience systematically appear in oculomotor behavior over time.

cs.HC↗

arXiv2025

Ocular Authentication: Fusion of Gaze and Periocular Modalities

This paper investigates the feasibility of fusing two eye-centric authentication modalities-eye movements and periocular images-within a calibration-free authentication system. While each modality has independently shown promise for user authentication, their combination within a unified gaze-estimation pipeline has not been thoroughly explored at scale. In this report, we propose a multimodal authentication system and evaluate it using a large-scale in-house dataset comprising 9202 subjects with an eye tracking (ET) signal quality equivalent to a consumer-facing virtual reality (VR) device. Our results show that the multimodal approach consistently outperforms both unimodal systems across all scenarios, surpassing the FIDO benchmark. The integration of a state-of-the-art machine learning architecture contributed significantly to the overall authentication performance at scale, driven by the model's ability to capture authentication representations and the complementary discriminative characteristics of the fused modalities.

cs.CV↗

arXiv2024

Evaluating Eye Tracking Signal Quality with Real-time Gaze Interaction Simulation

We present a real-time gaze-based interaction simulation methodology using an offline dataset to evaluate the eye-tracking signal quality. This study employs three fundamental eye-movement classification algorithms to identify physiological fixations from the eye-tracking data. We introduce the Rank-1 fixation selection approach to identify the most stable fixation period nearest to a target, referred to as the trigger-event. Our evaluation explores how varying constraints impact the definition of trigger-events and evaluates the eye-tracking signal quality of defined trigger-events. Results show that while the dispersion threshold-based algorithm identifies trigger-events more accurately, the Kalman filter-based classification algorithm performs better in eye-tracking signal quality, as demonstrated through a user-centric quality assessment using user- and error-percentile tiers. Despite median user-level performance showing minor differences across algorithms, significant variability in signal quality across participants highlights the importance of algorithm selection to ensure system reliability.

cs.HC↗

arXiv2024

Evidence for five types of fixation during a random saccade eye tracking task: Implications for the study of oculomotor fatigue

Our interest was to evaluate changes in fixation duration as a function of time-on-task (TOT) during a random saccade task. We employed a large, publicly available dataset. The frequency histogram of fixation durations was multimodal and modelled as a Gaussian mixture. We found five fixation types. The ``ideal'' response would be a single accurate saccade after each target movement, with a typical saccade latency of 200-250 msec, followed by a long fixation (> 800 msec) until the next target jump. We found fixations like this, but they comprised only 10% of all fixations and were the first fixation after target movement only 23.4% of the time. More frequently (57.4% of the time), the first fixation after target movement was short (117.7 msec mean) and was commonly followed by a corrective saccade. Across the entire 100 sec of the task, median total fixation duration decreased. This decrease was approximated with a power law fit with R^2=0.94. A detailed examination of the frequency of each of our five fixation types over time on task (TOT) revealed that the three shortest duration fixation types became more and more frequent with TOT whereas the two longest fixations became less and less frequent. In all cases, the changes over TOT followed power law relationships, with R^2 values between 0.73 and 0.93. We concluded that, over the 100 second duration of our task, long fixations are common in the first 15 to 22 seconds but become less common after that. Short fixations are relatively uncommon in the first 15 to 22 seconds but become more and more common as the task progressed. Apparently. the ability to produce an ideal response, although somewhat likely in the first 22 seconds, rapidly declines. This might be related to a noted decline in saccade accuracy over time.

q-bio.NC↗

arXiv2023

Determining Which Sine Wave Frequencies Correspond to Signal and Which Correspond to Noise in Eye-Tracking Time-Series

The Fourier theorem states that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some frequencies are important to reproduce key qualities of eye-movements ("signal") and some of frequencies are not important ("noise"). To investigate what is signal and what is noise, we analyzed our dataset in three ways: (1) visual inspection of plots of saccade, microsaccade and smooth pursuit exemplars; (2) analysis of the percentage of variance accounted for (PVAF) in 1,033 unfiltered saccade trajectories by each frequency band; (3) analyzing the main sequence relationship between saccade peak velocity and amplitude, based on a power law fit. Visual inspection suggested that frequencies up to 75 Hz are required to represent microsaccades. Our PVAF analysis indicated that signals in the 0-25 Hz band account for nearly 100% of the variance in saccade trajectories. Power law coefficients (a, b) return to unfiltered levels for signals low-pass filtered at 75 Hz or higher. We conclude that to maintain eye movement signal and reduce noise, a cutoff frequency of 75 Hz is appropriate. We explain why, given this finding, a minimum sampling rate of 750 Hz is suggested.

cs.HC↗

arXiv2023

Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters

In a previous report (Raju et al.,2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 100 Hz (-3db) were the signal and data above 100 Hz were noise. We compare 5 filters in their ability to preserve signal and remove noise. Specifically, we compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. Mean frequency response profiles and amplitude spectra for all 5 filters are provided. In addition, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. Several reports have shown that filtering increased the temporal autocorrelation of a signal. To address this, the present filters were also evaluated in terms of autocorrelation (specifically the first 3 lags). Of all our filters, the STD filter introduced the least amount of autocorrelation.

cs.HC↗

arXiv2023

Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes

Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As both measures emerge from statistical theory applied to time-series, there are certain statistical assumptions that accompany their use. It is intuitively obvious that the SD is most useful when applied to unimodal distributions. Both measures assume stationarity, which means that the statistical properties of the signals are stable over time. Both metrics assume the samples of the signals are independent. The presence of autocorrelation indicates that the samples in the time series are not independent. We tested these assumptions with multiple fixations from two studies, a publicly available dataset that included both human and artificial eyes ("HA Dataset", N=224 fixations), and data from our laboratory of 4 subjects ("TXstate", N=37 fixations). Many position signal distributions were multimodal (HA: median=32%, TXstate: median=100%). No fixation position signals were stationary. All position signals were statistically significantly autocorrelated (p < 0:01). Thus, the statistical assumptions of the SD were not met for any fixation. All StoS signals were unimodal. Some StoS signals were stationary (HA: 34%, TXstate: 24%). Almost all StoS signals were statistically significantly autocorrelated (p < 0:01). For TXstate, 3 of 37 fixations met all assumptions. Thus, the statistical assumptions of the RMS were generally not met. The general failure of these assumptions calls into question the appropriateness of the SD or the RMS-StoS as metrics of precision for eye-trackers.

q-bio.NC↗

arXiv2022

Analysis of Heuristic and Digital Filters as Applied to Video-oculography Signals

In 1993, Stampe [1993] suggested two "heurisitic" filters that were designed for video-oculography data. Several manufacturers (e.g., SR-Research, Tobii T60 XL and SMI) have employed these filters as an option for recording eye-movements. For the EyeLink family of eye-trackers, these two filters are referred to as standard (STD) or EXTRA. We have implemented these filters as software functions. For those who use their eye-trackers for data-collection only, this will allow users to collect unfiltered data and simultaneously have access to unfiltered, STD filtered and EXTRA filtered data for the exact same recording. Based on the literature, which has employed various eye-tracking technologies, and our analysis of our EyeLink-1000 data, we conclude that the highest signal frequency content needed for most eye-tracking studies (i.e., saccades, microsaccades and smooth pursuit) is around 100 Hz, excluding fixation microtremor. For those who collect their data at 1000 Hz or higher, we test two zero-phase low-pass digital filters, one with a cutoff of 50 Hz and one with a cutoff of 100 Hz. We perform a Fourier (FFT) analysis to examine the frequency content for unfiltered data, STD data, EXTRA filtered data, and data filtered by low-pass digital filters. We also examine the frequency response of these filters. The digital filter with the 100 Hz cutoff dramatically outperforms both heuristic filters because the heuristic filters leave noise above 100 Hz. In the paper we provide additional conclusions and suggest the use of digital filters in scenarios where offline data processing is an option.

cs.HC↗

arXiv2022

An Assessment of the Eye Tracking Signal Quality Captured in the HoloLens 2

We present an analysis of the eye tracking signal quality of the HoloLens 2s integrated eye tracker. Signal quality was measured from eye movement data captured during a random saccades task from a new eye movement dataset collected on 30 healthy adults. We characterize the eye tracking signal quality of the device in terms of spatial accuracy, spatial precision, temporal precision, linearity, and crosstalk. Most notably, our evaluation of spatial accuracy reveals that the eye movement data in our dataset appears to be uncalibrated. Recalibrating the data using a subset of our dataset task produces notably better eye tracking signal quality.

cs.HC↗

arXiv2020

Benefits of temporal information for appearance-based gaze estimation

State-of-the-art appearance-based gaze estimation methods, usually based on deep learning techniques, mainly rely on static features. However, temporal trace of eye gaze contains useful information for estimating a given gaze point. For example, approaches leveraging sequential eye gaze information when applied to remote or low-resolution image scenarios with off-the-shelf cameras are showing promising results. The magnitude of contribution from temporal gaze trace is yet unclear for higher resolution/frame rate imaging systems, in which more detailed information about an eye is captured. In this paper, we investigate whether temporal sequences of eye images, captured using a high-resolution, high-frame rate head-mounted virtual reality system, can be leveraged to enhance the accuracy of an end-to-end appearance-based deep-learning model for gaze estimation. Performance is compared against a static-only version of the model. Results demonstrate statistically-significant benefits of temporal information, particularly for the vertical component of gaze.

cs.CV↗

arXiv2020

OpenEDS2020: Open Eyes Dataset

We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras. The dataset, which is anonymized to remove any personally identifiable information on participants, consists of 80 participants of varied appearance performing several gaze-elicited tasks, and is divided in two subsets: 1) Gaze Prediction Dataset, with up to 66,560 sequences containing 550,400 eye-images and respective gaze vectors, created to foster research in spatio-temporal gaze estimation and prediction approaches; and 2) Eye Segmentation Dataset, consisting of 200 sequences sampled at 5 Hz, with up to 29,500 images, of which 5% contain a semantic segmentation label, devised to encourage the use of temporal information to propagate labels to contiguous frames. Baseline experiments have been evaluated on OpenEDS2020, one for each task, with average angular error of 5.37 degrees when performing gaze prediction on 1 to 5 frames into the future, and a mean intersection over union score of 84.1% for semantic segmentation. As its predecessor, OpenEDS dataset, we anticipate that this new dataset will continue creating opportunities to researchers in eye tracking, machine learning and computer vision communities, to advance the state of the art for virtual reality applications. The dataset is available for download upon request at http://research.fb.com/programs/openeds-2020-challenge/.

cs.CV↗