Pith. sign in

REVIEW 2 major objections 5 minor 1 cited by

Biometrics in Extended Reality: A Review

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This review claims to be the first systematic map of biometrics in extended reality, organizing attacks around three vulnerability points and classifying modalities and avatars into taxonomies.

desk verdict A useful but sloppy survey of biometrics in XR; the central vulnerability taxonomy is internally inconsistent as written, so it needs a real revision before it can be trusted. read the letter →

arxiv 2411.10489 v1 pith:OXLNJGU5 submitted 2024-11-14 cs.CR cs.AIcs.CV

Ayush Agarwal , Raghavendra Ramachandra , Sushma Venkatesh , S. R. Mahadeva Prasanna This is my paper · ORCID
classification cs.CRcs.AIcs.CV
keywords extendedrealitybiometricsauthenticationvirtualsecuritypresentationattacksavatargenerationbehavioralXRdatasets
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that biometrics in Extended Reality form a research area with a recognizable structure, and that the area has been missing a security map. It claims to supply that map for the first time, in the form of three taxonomies: vulnerability points in a biometric XR system, physiological and behavioral biometrics for authentication, and photorealistic avatar generation and verification. A sympathetic reader should care because commercial XR devices already capture iris and gaze data, and a photorealistic avatar is effectively a biometric credential that can be stolen or forged; without an agreed map, security measures, datasets, and benchmarks develop in isolation. If the survey is right, the field gains a common vocabulary and a checklist of attack surfaces to defend.

What carries the argument

The organizing device is the four-block XR workflow—input processor, simulation processor, rendering processor, XR environment—with three named vulnerability points. Vulnerability point 1 sits at initial authentication and is the home of presentation and shoulder-surfing attacks; vulnerability point 2 sits on the interaction and sensor channel and hosts side-channel and injection attacks; vulnerability point 3 sits in the rendered virtual space and hosts avatar impersonation, morphing, and deepfake attacks. The same device is paired with a two-axis biometric classification (physiological versus behavioral) and an avatar classification (2D versus 3D, static versus continuous, generation versus verification), so that each reviewed paper is slotted by which block of the workflow it touches and which gate it defends or exposes.

What would settle it

To test the priority claim, a reader could search the literature up to the paper's November 2024 cutoff for any earlier survey that already taxonomizes biometric vulnerability points in XR systems; if a pre-2024 paper presents the same three-gate model (device authentication, interaction channel, avatar space), the 'first' claim fails. To test the coverage claim, an independent team could reproduce the 62-paper shortlist from the stated keywords and screening steps; the reported pool sizes of 318 and 391 are inconsistent, so the shortlist is not currently auditable from the manuscript.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central discovery is that a biometric-enabled XR system has a small, enumerable set of attack gates, and that every reviewed technology can be placed at one of them. The first gate is initial device authentication in the physical world, where presentation attacks (printed artifacts, masks, replay) and shoulder surfing occur. The second is the channel that carries the user's captured motion and behavior into the virtual environment, where side-channel extraction and biometric sample injection occur. The third is the rendered virtual space, where an attacker can impersonate an avatar through man-in-the-room techniques, morphing, deepfakes, or stream exfiltration. The paper further claims that physiological biometrics (iris, periocular region, fingerveins, brain and heart signals) and behavioral biometrics (gaze, hand motion, gait, air handwriting, voice) are both in use in XR, and that deep-learning-generated photorealistic avatars now carry enough biometric fidelity to act as identity tokens. It concludes that secure XR needs layer-specific defenses at all three gates plus continuous avatar verification.

Load-bearing premise

The survey's usefulness and its 'first in the literature' claim rest on the 62 shortlisted papers being a fair and complete sample of XR-biometrics work, but the paper never lists those 62 papers or the reasons for exclusion, and its method section reports two different starting numbers, so a reader cannot currently check whether one missed prior survey or a skewed selection would have changed the taxonomy.

Editorial extensions

If this is right

  • Defenses can be assigned to distinct layers: anti-spoofing and liveness checks at device entry, tamper-resistant capture and encryption on the interaction channel, and continuous biometric verification of avatars inside the rendered scene.
  • Dataset construction can become systematic, with new XR biometric collections classified by modality and by the vulnerability point they stress, as the survey does for the pupil, iris, periocular, EEG, ECG, gaze, and gait datasets it lists.
  • Benchmarking should move beyond EER and accuracy toward XR-specific measures such as immersion score and latency, which the paper argues are needed to judge real-time authentication in a headset.
  • Commercial iris-authenticated headsets can be stress-tested against the enumerated presentation-attack instruments, since the survey identifies iris as an effective modality that remains vulnerable to presentation attacks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The three-gate model implies, though the paper does not say so directly, that continuous behavioral biometrics are the only layer able to verify identity after an avatar is rendered, because static enrollment credentials cannot follow the user into the scene.
  • A direct test of the survey's reproducibility would be to reconstruct the 62-paper shortlist from the reported search keywords and screening steps; the manuscript reports inconsistent starting pool sizes (318 versus 391 articles) and never lists the selected papers, so an independent check could confirm or overturn the coverage claim.
  • The vulnerability model transfers naturally to multi-user XR collaboration, where one participant's continuous authentication stream becomes another participant's attack surface—a scenario the survey leaves implicit in its man-in-the-room discussion.
  • The separation of avatar generation from avatar verification suggests an unstudied attack class: adversarially generating an avatar that preserves a victim's biometric identity well enough to pass face or gait verification while looking visually different to human observers.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This manuscript surveys biometrics in extended reality (XR), with sections on authentication modes, biometric vulnerability gateways and attacks, physiological/behavioral verification methods, photorealistic avatar generation and verification, datasets, and performance metrics. The paper is organized around four research questions and claims two firsts: a systematic discussion of biometric vulnerability gateways in XR with a taxonomy, and an exclusive survey of XR biometrics. The survey aggregates a broad set of references into tables of biometric modalities, datasets, avatar-generation methods, and prior surveys.

Significance. If corrected, the survey would address a real gap in the literature: prior reviews cover VR security, gaze privacy, or metaverse threats, but none ties the XR biometric workflow to a vulnerability taxonomy while also covering avatar generation and verification. The main value would be the organizing framework—the vulnerability gateways—and the consolidated tables of datasets and methods. The paper also makes a checkable priority claim ('first in the literature'), which becomes testable once the reviewed corpus is fully listed. The contribution is taxonomic and expository rather than computational, so the central quality bar is internal consistency of the taxonomy and reproducibility of the selection procedure; both currently need work.

major comments (2)
  1. [§2.3, Figure 6, §2.3.3, §7 RQ1] The central claim of a first systematic taxonomy of biometric vulnerability gateways cannot currently be enumerated from the paper as written. Section 2.3 states that 'we identified four different vulnerability points, as illustrated in Figure 6,' while the Figure 6 caption says 'In total, three vulnerability points need to be addressed.' The RQ1 answer in Section 7 likewise says 'we identified three primary points of vulnerability.' Section 2.3.3 is headed 'Vulnerability Point 3' but its final sentence promises 'we discuss the possible attacks at vulnerability point 4,' and no Vulnerability Point 4 definition or subsection appears anywhere. Please reconcile the count, align the figure caption with the text, and make the one-to-one mapping from each numbered vulnerability point to a subsection and figure element explicit.
  2. [Section 1, Methodology] The reported article pool is inconsistent and the shortlist is not auditable. Section 1 says 'we collected 318 articles from Google scholar,' but the second screening step says 'we conducted a comprehensive review of the 62 shortlisted papers out of the initial 391 articles.' The reader cannot determine whether 318 or 391 articles formed the initial corpus, and the 62 selected papers are never listed, nor are the exclusion criteria defined. This undermines the 'systematic' and 'comprehensive' claims and the priority claim, because a reader cannot verify the coverage without reconstructing the full search and screening. Please add a PRISMA-style flow diagram with exact counts, a complete list of the 62 shortlisted papers, and a description of inclusion/exclusion decisions.
minor comments (5)
  1. [Table 7] The Sun et al. [2022] 2023 Hand Movement row appears twice with identical sample counts; one duplicate should be removed.
  2. [Table 1] Table 1 lists Giaretta [2022] twice with different descriptions and labels De Guzman et al. [2019] under year 2023; please unify these entries and correct the years to match the reference list.
  3. [Tables 4 and 5 and surrounding text] Some attribution details are inconsistent: Table 4 lists Szymanowicz et al. [2023] for PointAvatar and PiCA, whereas the text attributes PointAvatar to Zheng et al. [2022] and PiCA to Ma et al. [2021]; Table 5 is also very terse for a survey that promises comprehensive coverage. Please align the table entries with the cited works.
  4. [Section 5.1] The paragraph beginning 'The The Brain signals...' contains a duplicated definite article; please correct this and similar typographical errors elsewhere, including 'formualating' (Section 1), 'Vulnerbality' (Section 2.3.3), 'Realilty' (Appendix), 'characterics' (Abstract and Section 3), and the spurious spaces in 'V oice' and 'V AE'.
  5. [Figure 11] The figure summarizing research-question answers should be harmonized with Section 7: the RQ1 box lists attack types rather than the 'three primary points of vulnerability' stated in the text, and the RQ4 box lists modalities rather than dataset categories, which makes the figure potentially misleading.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; minor self-citations are background only.

full rationale

This manuscript is an expository literature review, not a derivation or measurement paper. It contains no fitted parameters, no equations whose outputs are fed back as inputs, and no quantitative prediction that could be forced by construction. The load-bearing contribution is the organizing claim that the paper is the first systematic survey of biometrics in XR and the first taxonomy of XR biometric vulnerability gateways. That claim is a coverage and priority assertion; it is supported only by the authors' reading of the literature and by Table 1's comparison with prior surveys. A novelty claim of this kind can be wrong, but it is not circular unless the survey assumes the taxonomy it is presenting as evidence for itself, which it does not. The numerous self-citations (Ramachandra and Busch 2017a,b; Boutros et al. 2020a,b; Agarwal et al. 2020, 2022; Kotwal et al. 2024) are used as pointers to specific methods, datasets, and background definitions. None is load-bearing: the survey's conclusions do not depend on the truth of any of these cited works, and none is invoked to forbid an alternative taxonomy. The manuscript does contain internal inconsistencies that are properly correctness risks rather than circularity: the methodology reports 318 collected articles but says the second screening used 'the initial 391 articles'; Section 2.3 states 'we identified four different vulnerability points, as illustrated in Figure 6' while Figure 6's caption says 'In total, three vulnerability points need to be addressed' and the RQ1 answer in Section 7 says 'we identified three primary points of vulnerability.' These are enumeration and consistency defects in the presented taxonomy, not a case of an output being equivalent to an input by definition. Therefore the circularity score is 1, reflecting minor self-citation with no circular reasoning.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The survey's central claims rest on the representativeness of a 62-paper sample from an inconsistently reported search pool, on a four-component XR system model, and on an exhaustiveness assumption for its vulnerability taxonomy. No quantitative free parameters or invented entities are present.

assumptions (3)
  • domain assumption The 62 shortlisted papers, selected by the described keyword searches and two screening rounds, are representative of the full XR biometrics literature.
    Section 1 Methodology reports the search and screening but inconsistently states the initial pool (318 vs 391 articles) and does not list the 62 included papers, so representativeness is assumed.
  • domain assumption An XR system can be decomposed into four components: input processor, simulation processor, rendering processor, and XR environment.
    Section 2.1 and Figure 4 introduce this decomposition; all proposed vulnerability points are attached to it, but the decomposition is not derived or validated.
  • ad hoc to paper The proposed vulnerability point classification is exhaustive and disjoint across the XR biometric workflow.
    Section 2.3 defines three or four vulnerability points inconsistently (Figure 6 labels four while the RQ1 answer in Section 7 says three), so the taxonomy's boundaries are not settled.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Biometrics in Extended Reality: A Review." pith.science (2026). https://pith.science/paper/OXLNJGU5

@misc{pith2026241110489,
  author       = {Pith},
  title        = {Pith review of: Biometrics in Extended Reality: A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OXLNJGU5}},
  note         = {Machine review of arXiv:2411.10489}
}
read the original abstract

In the domain of Extended Reality (XR), particularly Virtual Reality (VR), extensive research has been devoted to harnessing this transformative technology in various real-world applications. However, a critical challenge that must be addressed before unleashing the full potential of XR in practical scenarios is to ensure robust security and safeguard user privacy. This paper presents a systematic survey of the utility of biometric characteristics applied in the XR environment. To this end, we present a comprehensive overview of the different types of biometric modalities used for authentication and representation of users in a virtual environment. We discuss different biometric vulnerability gateways in general XR systems for the first time in the literature along with taxonomy. A comprehensive discussion on generating and authenticating biometric-based photorealistic avatars in XR environments is presented with a stringent taxonomy. We also discuss the availability of different datasets that are widely employed in evaluating biometric authentication in XR environments together with performance evaluation metrics. Finally, we discuss the open challenges and potential future work that need to be addressed in the field of biometrics in XR.

Figures

Figures reproduced from arXiv: 2411.10489 by the authors.

Figure 1
Figure 1. Applications of extended reality (XR) systems in different sectors. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The number of year-wise works in the direction of security and privacy in VR applications indicates the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Taxonomical representation on the structure of this paper [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Illustrating the working principle of extended reality [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Types of authentication widely used in XR scenario that includes both knowledge based and biometrics. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: The first level of authentication can be performed on the device before using the device for XR applications. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 6
Figure 6. Figure 6: Illustration of various vulnerability of XR system in which the biometric characterics (physiological and [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: The taxonomic representation of biometric verification techniques in XR illustrates the applicability of both [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Taxonomy representing of different techniques used for avatar generation and verification in XR scenario. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: Evolution of avatars over time for the use of virtual reality environments. (a) and (b) shows the basic [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: The timeline of the development of avatars through generative deep learning models. [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Key answers to the proposed research questions [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Ocular Verification for Virtual Reality

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Standard ISO iris quality metrics behave inconsistently on VR-captured eyes (margin adequacy fails), while periocular-heavy score-level fusion reduces equal-error rate from 0.44 to 0.33 on VRBiom.

Reference graph

Works this paper leans on

52 extracted references · 46 canonical work pages · cited by 1 Pith paper

  1. [1]

    Virtual Reality: Blessings and Risk Assessment

    Ayush Sharma, Piyush Bajpai, Sukhdev Singh, and Kiran Khatter. Virtual reality: blessings and risk assessment. arXiv preprint arXiv:1708.09540,

    work page Pith review arXiv

  2. [3]

    Security of Virtual Reality Authentication Methods in Metaverse: An Overview

    Pınar Kürtünlüo˘glu, Beste Akdik, and Enis Karaarslan. Security of virtual reality authentication methods in metaverse: An overview. arXiv preprint arXiv:2209.06447,

    work page Pith review arXiv

  3. [4]

    Security and privacy in metaverse: A comprehensive survey

    Yan Huang, Yi Joy Li, and Zhipeng Cai. Security and privacy in metaverse: A comprehensive survey. Big Data Mining and Analytics, 6(2):234–247, 2023a. Efe Bozkir, Süleyman Özdel, Mengdi Wang, Brendan David-John, Hong Gao, Kevin Butler, Eakta Jain, and Enkelejda Kasneci. Eye-tracked virtual reality: A comprehensive survey on methods and privacy challenges. ...

    arXiv

  4. [11]

    Brain signal authentication for human-computer interaction in virtual reality

    Sukun Li, Sonal Savaliya, Leonard Marino, Avery M Leider, and Charles C Tappert. Brain signal authentication for human-computer interaction in virtual reality. In 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pages 115–120. IEEE,

    work page 2019

  5. [13]

    Fake speech detection using modulation spectrogram

    Raghav Magazine, Ayush Agarwal, Anand Hedge, and SR Mahadeva Prasanna. Fake speech detection using modulation spectrogram. In Speech and Computer: 24th International Conference, SPECOM 2022, Gurugram, India, November 14–16, 2022, Proceedings, pages 451–463. Springer,

    work page 2022

  6. [14]

    Vr-spy: A side-channel attack on virtual key-logging in vr headsets

    Abdullah Al Arafat, Zhishan Guo, and Amro Awad. Vr-spy: A side-channel attack on virtual key-logging in vr headsets. In 2021 IEEE Virtual Reality and 3D User Interfaces (VR), pages 564–572. IEEE,

    work page 2021

  7. [16]

    Vrcauth: continuous authentication of users in virtual reality environment using head-movement

    Manimaran Sivasamy, VN Sastry, and NP Gopalan. Vrcauth: continuous authentication of users in virtual reality environment using head-movement. In 2020 5th International Conference on Communication and Electronics Systems (ICCES), pages 518–523. IEEE,

    work page 2020

  8. [18]

    Put your glasses on: A voxel-based 3d authentication system in vr using eye-gaze

    Rumeysa Turkmen, Chukwuemeka Nwagu, Prashant Rawat, Poppy Riddle, Kissinger Sunday, and Mayra Barrera Machuca. Put your glasses on: A voxel-based 3d authentication system in vr using eye-gaze. In 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pages 947–948. IEEE,

    work page 2023

Show all 52 references
  1. [19]

    A securing access approach to virtual worlds based on 3d mesh watermarking of avatar’s face

    Samira Bader and Najoua Essoukri Ben Amara. A securing access approach to virtual worlds based on 3d mesh watermarking of avatar’s face. In 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA), pages 1–6. IEEE, 2014a. S Bader and N Essouk...

    2014

  2. [20]

    Oculock: Exploring human visual system for authentication in virtual reality head-mounted display

    Shiqing Luo, Anh Nguyen, Chen Song, Feng Lin, Wenyao Xu, and Zhisheng Yan. Oculock: Exploring human visual system for authentication in virtual reality head-mounted display. In 2020 Network and Distributed System Security Symposium (NDSS),

    2020

  3. [21]

    A deep learning approach to segmentation of distorted iris regions in head-mounted displays

    Viktor Varkarakis, Shabab Bazrafkan, and Peter Corcoran. A deep learning approach to segmentation of distorted iris regions in head-mounted displays. In 2018 IEEE Games, Entertainment, Media Conference (GEM), pages 1–9. IEEE,

    2018

  4. [22]

    Questsim: Human motion tracking from sparse sensors with simulated avatars

    Alexander Winkler, Jungdam Won, and Yuting Ye. Questsim: Human motion tracking from sparse sensors with simulated avatars. In SIGGRAPH Asia 2022 Conference Papers, pages 1–8,

    2022

  5. [23]

    Demonstrating eye movement biometrics in virtual reality

    Dillon Lohr, Saide Johnson, Samantha Aziz, and Oleg Komogortsev. Demonstrating eye movement biometrics in virtual reality. arXiv preprint arXiv:2207.02325,

    arXiv

  6. [24]

    Mariusz Wierzbowski, Grzegorz Pochwatko, Paulina Borkiewicz, Daniel Cnotkowski, Michał Pabi´s-Orzeszyna, and Paweł Kobyli´nski. Behavioural biometrics in virtual reality: To what extent can we identify a person based solely on how they watch 360-degree videos? In 2022 IEEE Int...

    2022

  7. [25]

    doi:10.3390/virtualworlds1010004

    ISSN 2813-2084. doi:10.3390/virtualworlds1010004. URL https://www.mdpi.com/2813-2084/1/1/4. Jonathan Liebers, Sascha Brockel, Uwe Gruenefeld, and Stefan Schneegass. Identifying users by their hand tracking data in augmented and virtual reality. International Journal of Human–C...

    doi

  8. [26]

    Temporal effects in motion behavior for virtual reality (vr) biometrics

    Robert Miller, Natasha Kholgade Banerjee, and Sean Banerjee. Temporal effects in motion behavior for virtual reality (vr) biometrics. In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pages 563–572. IEEE,

    2022

  9. [27]

    Biomove: Biometric user identification from human kinesiolog- ical movements for virtual reality systems

    Ilesanmi Olade, Charles Fleming, and Hai-Ning Liang. Biomove: Biometric user identification from human kinesiolog- ical movements for virtual reality systems. Sensors, 20(10):2944, 2020b. Ken Pfeuffer, Matthias J Geiger, Sarah Prange, Lukas Mecke, Daniel Buschek, and Florian A...

    2019

  10. [28]

    Multifactor user authentication with in-air-handwriting and hand geometry

    Duo Lu, Dijiang Huang, Yuli Deng, and Adel Alshamrani. Multifactor user authentication with in-air-handwriting and hand geometry. In 2018 International Conference on Biometrics (ICB), pages 255–262. IEEE,

    2018

  11. [29]

    Fmhash: Deep hashing of in-air-handwriting for user identification

    Duo Lu, Dijiang Huang, and Anshul Rai. Fmhash: Deep hashing of in-air-handwriting for user identification. In ICC 2019-2019 IEEE International Conference on Communications (ICC), pages 1–7. IEEE,

    2019

  12. [30]

    Fmkit: An in-air-handwriting analysis library and data repository

    Duo Lu and Linzhen Luo. Fmkit: An in-air-handwriting analysis library and data repository. In CVPR Workshop on Computer Vision for Augmented and Virtual Reality, 2020,

    2020

  13. [31]

    Global feature analysis and comparative evaluation of freestyle in-air- handwriting passcode for user authentication

    Duo Lu, Yuli Deng, and Dijiang Huang. Global feature analysis and comparative evaluation of freestyle in-air- handwriting passcode for user authentication. In Annual Computer Security Applications Conference, pages 468–481, 2021a. Yung-Hui Li, Po-Jen Huang, et al. An accurate ...

    2017

  14. [32]

    Enabling secure voice input on augmented reality headsets using internal body voice

    Jiacheng Shang and Jie Wu. Enabling secure voice input on augmented reality headsets using internal body voice. In 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pages 1–9. IEEE,

    2019

  15. [33]

    A practical mimicry attack against powerful system-call monitors

    Chetan Parampalli, R Sekar, and Rob Johnson. A practical mimicry attack against powerful system-call monitors. In Proceedings of the 2008 ACM symposium on Information, computer and communications security, pages 156–167,

    2008

  16. [34]

    An implementation of eye movement-driven biometrics in virtual reality

    Dillon Lohr, Samuel-Hunter Berndt, and Oleg Komogortsev. An implementation of eye movement-driven biometrics in virtual reality. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, pages 1–3,

    2018

  17. [35]

    Towards the automatic generation of 3d photo-realistic avatars using 3d scanned data

    Thibault Luginbühl, Laurent Delattre, and André Gagalowicz. Towards the automatic generation of 3d photo-realistic avatars using 3d scanned data. In Computer Vision/Computer Graphics Collaboration Techniques: 5th International Conference, MIRAGE 2011, Rocquencourt, France, Oct...

    2011

  18. [37]

    Photorealistic interactive virtual environment generation using multiview cameras

    Namgyu Kim, Woontack Woo, and Makoto Tadenuma. Photorealistic interactive virtual environment generation using multiview cameras. In Visual Communications and Image Processing 2001, volume 4310, pages 245–254. SPIE,

    2001

  19. [39]

    Photo-realistic 360 head avatars in the wild

    Stanislaw Szymanowicz, Virginia Estellers, Tadas Baltrušaitis, and Matthew Johnson. Photo-realistic 360 head avatars in the wild. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III, pages 660–667. Springer,

    2022

  20. [40]

    Agileavatar: Stylized 3d avatar creation via cascaded domain bridging

    Shen Sang, Tiancheng Zhi, Guoxian Song, Minghao Liu, Chunpong Lai, Jing Liu, Xiang Wen, James Davis, and Linjie Luo. Agileavatar: Stylized 3d avatar creation via cascaded domain bridging. In SIGGRAPH Asia 2022 Conference Papers, pages 1–8,

    2022

  21. [41]

    Humannorm: Learning normal diffusion model for high-quality and realistic 3d human generation

    Xin Huang, Ruizhi Shao, Qi Zhang, Hongwen Zhang, Ying Feng, Yebin Liu, and Qing Wang. Humannorm: Learning normal diffusion model for high-quality and realistic 3d human generation. arXiv preprint arXiv:2310.01406, 2023b. Sadegh Aliakbarian, Fatemeh Saleh, David Collier, Pashmi...

    arXiv

  22. [42]

    Evaluation of svm classification of avatar facial recognition

    Sonia Ajina, Roman V Yampolskiy, and Najoua Essoukri Ben Amara. Evaluation of svm classification of avatar facial recognition. In Advances in Neural Networks–ISNN 2011: 8th International Symposium on Neural Networks, ISNN 2011, Guilin, China, May 29–June 1, 2011, Proceedings, ...

    2011

  23. [43]

    Face recognition in the virtual world: recognizing avatar faces

    32 Biometrics in Extended Reality: A Review A PREPRINT Roman V Yampolskiy, Brendan Klare, and Anil K Jain. Face recognition in the virtual world: recognizing avatar faces. In 2012 11th International Conference on Machine Learning and Applications, volume 1, pages 40–45. IEEE, ...

    2012 arXiv

  24. [45]

    Nvgaze: An anatomically-informed dataset for low-latency, near-eye gaze estimation

    Joohwan Kim, Michael Stengel, Alexander Majercik, Shalini De Mello, David Dunn, Samuli Laine, Morgan McGuire, and David Luebke. Nvgaze: An anatomically-informed dataset for low-latency, near-eye gaze estimation. In Proceedings of the 2019 CHI conference on human factors in com...

    2019

  25. [46]

    Vrbiom: A new periocular dataset for biometric applications of hmd

    Ketan Kotwal, Ibrahim Ulucan, Gokhan Ozbulak, Janani Selliah, and Sebastien Marcel. Vrbiom: A new periocular dataset for biometric applications of hmd. arXiv preprint arXiv:2407.02150,

    arXiv

  26. [47]

    Openeds: Open eye dataset

    Stephan J Garbin, Yiru Shen, Immo Schuetz, Robert Cavin, Gregory Hughes, and Sachin S Talathi. Openeds: Open eye dataset. arXiv preprint arXiv:1905.03702,

    1905 arXiv

  27. [48]

    Multimodal biometric authentication for vr/ar using eeg and eye tracking

    Vrishab Krishna, Yi Ding, Aiwen Xu, and Tobias Höllerer. Multimodal biometric authentication for vr/ar using eeg and eye tracking. In Adjunct of the 2019 International Conference on Multimodal Interaction, pages 1–5,

    2019

  28. [49]

    Sdumla-hmt: a multimodal biometric database

    Yilong Yin, Lili Liu, and Xiwei Sun. Sdumla-hmt: a multimodal biometric database. In Biometric Recognition: 6th Chinese Conference, CCBR 2011, Beijing, China, December 3-4,

    2011

  29. [50]

    A database with roi extraction for studying fusion of finger vein and finger dorsal texture

    Wenming Yang, Chuan Qin, and Qingmin Liao. A database with roi extraction for studying fusion of finger vein and finger dorsal texture. In Biometric Recognition: 9th Chinese Conference, CCBR 2014, Shenyang, China, November 7-9,

    2014

  30. [51]

    Synchroneyes: A novel, paired data set of eye movements recorded simultaneously with remote and wearable eye-tracking devices

    Samantha Aziz, Dillon J Lohr, and Oleg Komogortsev. Synchroneyes: A novel, paired data set of eye movements recorded simultaneously with remote and wearable eye-tracking devices. In 2022 Symposium on Eye Tracking Research and Applications, pages 1–6,

    2022

  31. [2000]

    Ag3d: Learning to generate 3d avatars from 2d image collections

    Zijian Dong, Xu Chen, Jinlong Yang, Michael J Black, Otmar Hilliges, and Andreas Geiger. Ag3d: Learning to generate 3d avatars from 2d image collections. arXiv preprint arXiv:2305.02312,

    arXiv

  32. [2008]

    Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition

    Abhishek Gangwar and Akanksha Joshi. Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition. In 2016 IEEE international conference on image processing (ICIP), pages 2301–2305. IEEE,

    2016

  33. [2009]

    Sid-avatar database: A 3d avatar dataset for virtual world research

    Samira Bader and Najoua Esssoukri Ben Amara. Sid-avatar database: A 3d avatar dataset for virtual world research. In International Image Processing, Applications and Systems Conference, pages 1–5. IEEE, 2014b. 33 Biometrics in Extended Reality: A Review A PREPRINT Jyh-Min Chen...

    2004

  34. [2011]

    Just-in-time, viable, 3d avatars from scans

    Andrew Feng, Evan Suma, and Ari Shapiro. Just-in-time, viable, 3d avatars from scans. In ACM SIGGRAPH 2017 Talks, pages 1–2

    2017

  35. [2014]

    Dynamic keypad–digit shuffling for secure pin entry in a virtual world

    Andrew Holland and Tony Morelli. Dynamic keypad–digit shuffling for secure pin entry in a virtual world. In Virtual, Augmented and Mixed Reality: Interaction, Navigation, Visualization, Embodiment, and Simulation: 10th International Conference, VAMR 2018, Held as Part of HCI I...

    2018

  36. [2016]

    Metasecure: A passwordless authentication for the metaverse

    Sibi Chakkaravarthy Sethuraman, Aditya Mitra, Anisha Ghosh, Gautam Galada, and Anitha Subramanian. Metasecure: A passwordless authentication for the metaverse. arXiv preprint arXiv:2301.01770,

    arXiv

  37. [2017]

    Exploring the vulnerabilities and advantages of swipe or pattern authentication in virtual reality (vr)

    Ilesanmi Olade, Hai-Ning Liang, Charles Fleming, and Christopher Champion. Exploring the vulnerabilities and advantages of swipe or pattern authentication in virtual reality (vr). In Proceedings of the 2020 4th International Conference on Virtual and Augmented Reality Simulati...

    2020

  38. [2018]

    Rubikauth: Fast and secure authentication in virtual reality

    Florian Mathis, John Williamson, Kami Vaniea, and Mohamed Khamis. Rubikauth: Fast and secure authentication in virtual reality. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pages 1–9, 2020a. Florian Mathis, Hassan Ismail Fawaz, and Mo...

    2020

  39. [2019]

    Fusing iris and periocular region for user verification in head mounted displays

    Fadi Boutros, Naser Damer, Kiran Raja, Raghavendra Ramachandra, Florian Kirchbuchner, and Arjan Kuijper. Fusing iris and periocular region for user verification in head mounted displays. In2020 IEEE 23rd International Conference on Information Fusion (FUSION), pages 1–8. IEEE,...

    2019

  40. [2020]

    Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification

    Brecht Desplanques, Jenthe Thienpondt, and Kris Demuynck. Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification. arXiv preprint arXiv:2005.07143,

    2005 arXiv

  41. [2021]

    Mixed-sense: A mixed reality sensor emulation framework for test and evaluation of uavs against false data injection attacks

    Kartik A Pant, Li-Yu Lin, Jaehyeok Kim, Worawis Sribunma, James M Goppert, and Inseok Hwang. Mixed-sense: A mixed reality sensor emulation framework for test and evaluation of uavs against false data injection attacks. arXiv preprint arXiv:2407.09342,

    arXiv

  42. [2022]

    Security and privacy in virtual reality–a literature survey

    Alberto Giaretta. Security and privacy in virtual reality–a literature survey. arXiv preprint arXiv:2205.00208,

    arXiv

  43. [2023]

    Biometric as secure authentication for virtual reality environment: A systematic literature review

    27 Biometrics in Extended Reality: A Review A PREPRINT Chandra Hermawan Heruatmadja, Achmad Nizar Hidayanto, Harjanto Prabowo, et al. Biometric as secure authentication for virtual reality environment: A systematic literature review. In 2023 International Conference for Advanc...

    2023

  44. [2024]

    Presentation attack detection methods for face recognition systems: A comprehensive survey

    Raghavendra Ramachandra and Christoph Busch. Presentation attack detection methods for face recognition systems: A comprehensive survey. ACM Comput. Surv., 50(1), mar 2017a. ISSN 0360-0300. doi:10.1145/3038924. URL https://doi.org/10.1145/3038924. Sébastien Marcel, Mark S Nixo...

    doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.