Image-based Face Verification for Student Identity Management - the TRUSTID Case Study

José Nuno Faria; David Portugal; Pedro Martins; Marios Belk; Argyris Constantinides; Andreas Pitsillides; Christos Fidas

doi:10.1145/3563359.3597397

Image-based Face Verification for Student Identity Management - the TRUSTID Case Study

José Nuno Faria
, David Portugal
, Pedro Martins
, Marios Belk
, Argyris Constantinides
, Andreas Pitsillides
, Christos Fidas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Managing attendance and confirming student identity in online lessons is a contemporary problem of Higher Education Institutions (HEIs) that comes with its challenges, especially in classes with large numbers of students. The development and deployment of fully automatic and continuous image-based face verification techniques may provide a natural solution for this problem and assist instructors in handling the complexity of managing student identity in a remote classroom. In this paper, we present an automatic image-based student identification framework. Our approach proposes a biometric authentication platform using a Residual Network (ResNet) Learning method for face verification. The system described is intended for use with consumer grade web cameras, providing a tradeoff between reliability and computational performance, as no assumption can be done regarding the target student hardware (i.e. CPU or GPU). This follows from the case study of TRUSTID, an European R&D initiative for intelligent student identity management in distance learning scenarios.

Original language	English
Title of host publication	UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
Publisher	Association for Computing Machinery, Inc
Pages	66-71
Number of pages	6
ISBN (Electronic)	9781450398916
DOIs	https://doi.org/10.1145/3563359.3597397
Publication status	Published - 26 Jun 2023
Externally published	Yes
Event	31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023 - Limassol, Cyprus Duration: 26 Jun 2023 → 30 Jun 2023

Publication series

Name	UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference	31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023
Country/Territory	Cyprus
City	Limassol
Period	26/06/23 → 30/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Keywords

Face Verification
Image Processing
Student Identity Management

ASJC Scopus subject areas

Software

Access to Document

10.1145/3563359.3597397

Cite this

Faria, J. N., Portugal, D., Martins, P., Belk, M., Constantinides, A., Pitsillides, A., & Fidas, C. (2023). Image-based Face Verification for Student Identity Management - the TRUSTID Case Study. In UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 66-71). (UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery, Inc. https://doi.org/10.1145/3563359.3597397

Faria, José Nuno ; Portugal, David ; Martins, Pedro et al. / Image-based Face Verification for Student Identity Management - the TRUSTID Case Study. UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, 2023. pp. 66-71 (UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization).

@inproceedings{34f2152147c04e05a1d705743df5965a,

title = "Image-based Face Verification for Student Identity Management - the TRUSTID Case Study",

abstract = "Managing attendance and confirming student identity in online lessons is a contemporary problem of Higher Education Institutions (HEIs) that comes with its challenges, especially in classes with large numbers of students. The development and deployment of fully automatic and continuous image-based face verification techniques may provide a natural solution for this problem and assist instructors in handling the complexity of managing student identity in a remote classroom. In this paper, we present an automatic image-based student identification framework. Our approach proposes a biometric authentication platform using a Residual Network (ResNet) Learning method for face verification. The system described is intended for use with consumer grade web cameras, providing a tradeoff between reliability and computational performance, as no assumption can be done regarding the target student hardware (i.e. CPU or GPU). This follows from the case study of TRUSTID, an European R\&D initiative for intelligent student identity management in distance learning scenarios.",

keywords = "Face Verification, Image Processing, Student Identity Management",

author = "Faria, \{Jos{\'e} Nuno\} and David Portugal and Pedro Martins and Marios Belk and Argyris Constantinides and Andreas Pitsillides and Christos Fidas",

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year = "2023",

month = jun,

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doi = "10.1145/3563359.3597397",

language = "English",

series = "UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization",

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}

Faria, JN, Portugal, D, Martins, P, Belk, M, Constantinides, A, Pitsillides, A & Fidas, C 2023, Image-based Face Verification for Student Identity Management - the TRUSTID Case Study. in UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization. UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, Inc, pp. 66-71, 31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023, Limassol, Cyprus, 26/06/23. https://doi.org/10.1145/3563359.3597397

Image-based Face Verification for Student Identity Management - the TRUSTID Case Study. / Faria, José Nuno; Portugal, David; Martins, Pedro et al.
UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, 2023. p. 66-71 (UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Image-based Face Verification for Student Identity Management - the TRUSTID Case Study

AU - Faria, José Nuno

AU - Portugal, David

AU - Martins, Pedro

AU - Belk, Marios

AU - Constantinides, Argyris

AU - Pitsillides, Andreas

AU - Fidas, Christos

PY - 2023/6/26

Y1 - 2023/6/26

N2 - Managing attendance and confirming student identity in online lessons is a contemporary problem of Higher Education Institutions (HEIs) that comes with its challenges, especially in classes with large numbers of students. The development and deployment of fully automatic and continuous image-based face verification techniques may provide a natural solution for this problem and assist instructors in handling the complexity of managing student identity in a remote classroom. In this paper, we present an automatic image-based student identification framework. Our approach proposes a biometric authentication platform using a Residual Network (ResNet) Learning method for face verification. The system described is intended for use with consumer grade web cameras, providing a tradeoff between reliability and computational performance, as no assumption can be done regarding the target student hardware (i.e. CPU or GPU). This follows from the case study of TRUSTID, an European R&D initiative for intelligent student identity management in distance learning scenarios.

AB - Managing attendance and confirming student identity in online lessons is a contemporary problem of Higher Education Institutions (HEIs) that comes with its challenges, especially in classes with large numbers of students. The development and deployment of fully automatic and continuous image-based face verification techniques may provide a natural solution for this problem and assist instructors in handling the complexity of managing student identity in a remote classroom. In this paper, we present an automatic image-based student identification framework. Our approach proposes a biometric authentication platform using a Residual Network (ResNet) Learning method for face verification. The system described is intended for use with consumer grade web cameras, providing a tradeoff between reliability and computational performance, as no assumption can be done regarding the target student hardware (i.e. CPU or GPU). This follows from the case study of TRUSTID, an European R&D initiative for intelligent student identity management in distance learning scenarios.

KW - Face Verification

KW - Image Processing

KW - Student Identity Management

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U2 - 10.1145/3563359.3597397

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M3 - Conference contribution

AN - SCOPUS:85163770648

T3 - UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

SP - 66

EP - 71

BT - UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

PB - Association for Computing Machinery, Inc

T2 - 31st ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2023

Y2 - 26 June 2023 through 30 June 2023

ER -

Faria JN, Portugal D, Martins P, Belk M, Constantinides A, Pitsillides A et al. Image-based Face Verification for Student Identity Management - the TRUSTID Case Study. In UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc. 2023. p. 66-71. (UMAP 2023 - Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization). doi: 10.1145/3563359.3597397

Image-based Face Verification for Student Identity Management - the TRUSTID Case Study

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this