Ocular Verification for Virtual Reality

• URL: http://arxiv.org/abs/2607.20790v1
• Date: Wed, 22 Jul 2026 23:28:10 GMT
• Title: Ocular Verification for Virtual Reality
• Abstract summary: Virtual reality (VR) headsets provide a seamless user experience due to their fast, frictionless interaction with the physical world in a simulated environment.<n>User authentication relies on biometric cues such as iris in such headsets.<n>Traditional iris recognition protocols may not be adequate in cases of unconstrained acquisition, which is typical of VR-based data.
• Score: 1.4932780345357946
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Virtual reality (VR) headsets (e.g., Meta Quest, Apple Vision Pro) provide a seamless user experience due to their fast, frictionless interaction with the physical world in a simulated environment. User authentication relies on biometric cues such as iris in such headsets. However, traditional iris recognition protocols may not be adequate in cases of unconstrained acquisition, which is typical of VR-based data. In this work, we examine three crucial aspects: (1) evaluating ISO/IEC 29794-6 iris quality metrics on VRBiom dataset and analyzing their limitations, (2) addressing data-specific challenges such as off-axis gaze, non-uniform illumination, and specular reflection using generative models, and (3) performing unimodal (iris, periocular) recognition and multimodal score-level fusion (iris + periocular). We observe that some metrics (e.g., margin adequacy) fail on VR-acquired data; whereas, image adjustments primarily benefit periocular recognition, and multimodal fusion lowers EER by ~11% over unimodal iris recognition performance. We will release the evaluation scripts upon acceptance for reproducibility.

Related papers

• ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality [4.439002023615223]
  We present ARIQA-3DS, the first large stereoscopic AR Image Quality Assessment dataset.<n>The dataset fuses high-resolution stereoscopic omni captures of real-world scenes with diverse augmented foregrounds under controlled transparency and degradation conditions.<n>Our analysis reveals that perceived quality is primarily driven by foreground degradation and modulated by transparency levels, while oculomotor and perceptual symptoms show a progressive but manageable increase during viewing.
  arXiv  Detail & Related papers  (2026-04-03T15:35:27Z)
• Achieving Effective Virtual Reality Interactions via Acoustic Gesture Recognition based on Large Language Models [11.630591232366255]
  Vision-based gesture recognition suffers from high computational cost, sensitivity to lighting conditions, and privacy leakage concerns.<n> Acoustic sensing provides an attractive alternative: by emitting inaudible high-frequency signals and capturing their reflections, channel impulse response (CIR) encodes how gestures perturb the acoustic field in a low-cost and user-transparent manner.<n>We propose the first framework that leverages large language models (LLMs) for CIR-based gesture recognition in VR/AR systems.
  arXiv  Detail & Related papers  (2025-11-10T13:19:58Z)
• TransFIRA: Transfer Learning for Face Image Recognizability Assessment [73.61309363885552]
  TransFIRA is a lightweight and annotation-free framework that grounds recognizability directly in embedding space.<n>New extensions beyond faces include encoder-grounded explainability that reveals how degradations and subject-specific factors affect recognizability.<n> Experiments confirm state-of-the-art results on faces, strong robustness on body recognition, and under cross-dataset shifts.
  arXiv  Detail & Related papers  (2025-10-07T18:16:21Z)
• EyeSeg: An Uncertainty-Aware Eye Segmentation Framework for AR/VR [58.33693755009173]
  EyeSeg is an uncertainty-aware eye segmentation framework for augmented reality (AR) and virtual reality (VR)<n>We show that EyeSeg achieves segmentation improvements of MIoU, E1, F1, and ACC surpassing previous approaches.
  arXiv  Detail & Related papers  (2025-07-13T14:33:10Z)
• Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait [70.00430652562012]
  FarSight is an end-to-end system for person recognition that integrates biometric cues across face, gait, and body shape modalities.<n>FarSight incorporates novel algorithms across four core modules: multi-subject detection and tracking, recognition-aware video restoration, modality-specific biometric feature encoding, and quality-guided multi-modal fusion.
  arXiv  Detail & Related papers  (2025-05-07T17:58:25Z)
• Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study [44.44776028287441]
  We benchmark blurring, noising, downsampling, rubber sheet model, and iris style transfer to obfuscate user identity.<n>Our experiments show that canonical image processing methods like blurring and noising cause a marginal impact on deep learning-based tasks.<n>While downsampling, rubber sheet model, and iris style transfer are effective in hiding user identifiers, iris style transfer, with higher computation cost, outperforms others in both utility tasks.
  arXiv  Detail & Related papers  (2025-04-14T14:29:38Z)
• Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience [11.130411904676095]
  Eye tracking data, if exposed, can be used for re-identification attacks. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications.
  arXiv  Detail & Related papers  (2024-02-12T14:53:12Z)
• Physically-Based Face Rendering for NIR-VIS Face Recognition [165.54414962403555]
  Near infrared (NIR) to Visible (VIS) face matching is challenging due to the significant domain gaps. We propose a novel method for paired NIR-VIS facial image generation. To facilitate the identity feature learning, we propose an IDentity-based Maximum Mean Discrepancy (ID-MMD) loss.
  arXiv  Detail & Related papers  (2022-11-11T18:48:16Z)
• A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition [53.41825941088989]
  A new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition. Our results show that WiVi data set satisfies the primary demand and all three branches in the proposed pipeline keep more than $80%$ of activity recognition accuracy.
  arXiv  Detail & Related papers  (2022-05-24T10:49:11Z)
• Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning [96.72850130126294]
  We propose an Iris U-transformer (IrisUsformer) for accurate and reliable iris segmentation. For better accuracy, we elaborately design IrisUsformer by adopting position-sensitive operation and re-packaging transformer block. We show that IrisUsformer achieves better segmentation accuracy using 35% MACs of the SOTA IrisParseNet.
  arXiv  Detail & Related papers  (2021-10-20T01:37:19Z)
• On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Application [16.382021536377437]
  We evaluate a set of iris recognition algorithms suitable for Head-Mounted Displays (HMD) We employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD.
  arXiv  Detail & Related papers  (2020-10-20T17:05:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.