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Interacting Face Detection-based Access Control with Various Authentication Factors

Published:18 June 2021Publication History

ABSTRACT

Today, biometric authentication is used to identify people who want to gain access to a highly sensitive system. The reasons are the need for high level of security and ease of use. In addition, the COVID-19 crisis has escalated digital transformation, promoting contactless methods for hygiene purposes. Although biometric factors can include any parts of the body, their use may encounter privacy issues. Facial data is one of the most acceptable biometric factors that people are willing to give as opposed to others such as fingerprints, retina, iris, and vein. However, a biometric approach alone cannot reach an acceptable level of security and ease of use. Other authentication factor methods are needed to integrate into the existing face detection-based method. In this paper, we will present a review study on face detection-based access control along with the use of various factors found during the COVID-19 pandemic. The study presents a framework to implement the system while identifying additional constraints including health, safety, personal data protection and seamless process, which inform a new design of the system architecture.

References

  1. Caroline Lancelot Miltgen, Aleš Popovič, and Tiago Oliveira, “Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context,” Decision Support Systems, 56, 103-114, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  2. Luke Dormehl, “Facial recognition: is the technology taking away your identity?” The Guardian (4th May 2014), 2014.Google ScholarGoogle Scholar
  3. Timo Ahonen, Abdenour Hadid, and Matti Pietikäinen, “Face recognition with local binary patterns,” In European conference on computer vision, pages 469–481. Springer, 2004.Google ScholarGoogle Scholar
  4. Karen Simonyan, Omkar. M. Parkhi, Andrea Vedaldi, and Andrew Zisserman, “Fisher vector faces in the wild,” In BMVC, volume 2, page 4, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  5. Matthew Turk and Alex Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience 1991; 3(1):71–86.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Baback Moghaddam, Tony Jebara, and Alex Pentland Moghaddam, “Bayesian face recognition,” Pattern recognition 2000; 33(11):1771–1782.Google ScholarGoogle ScholarCross RefCross Ref
  7. Guodong Guo, Stan Z. Li, and Kapluk Chan, “Face recognition by support vector machines,” In IEEE international conference on automatic face and gesture recognition (cat. no. PR00580) 2000 (pp. 196–201).Google ScholarGoogle Scholar
  8. Guo-Dong Guo, and Hong-Jiang Zhang, “Boosting for fast face recognition,” In IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems 2001 (pp. 96–100).Google ScholarGoogle Scholar
  9. Florian Schroff, Dmitry Kalenichenko, and James Philbin, “A unfied embedding for face recognition and clustering,” In IEEE Conference on Computer Vision and Pattern Recognition, page 815-823, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  10. Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf, “Deepface: Closing the gap to human-level performance in face verification,” In IEEE Conference on Computer Vision and Pattern Recognition, pages 1701-1708, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Aruni Roy Chowdhury, Tsung-Yu Lin, Subhransu Maji, and Erik Learned-Miller, “One-to-many face recognition with bilinear cnns,” In IEEE Winter Conference on Applications of Computer Vision (WACV) 2016 (pp. 1–9).Google ScholarGoogle Scholar
  12. Omkar. M. Parkhi, Andrea Vedaldi, and Andrew Zisserman, “Deep face recognition,” In British Machine Vision Conference, volume 1, page 6, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  13. Patrick J. Grother and Mei Ngan, “Face recognition vendor test (FRVT): Performance of face identification algorithms,” In NIST Interagency report 8009, 2014.Google ScholarGoogle Scholar
  14. The Institute's Editorial Staff, “This Temperature-Screening System for COVID-19 Can Check Up to 9 People at Once,”, IEEE Spectrum, https://spectrum.ieee.org/, 2020.Google ScholarGoogle Scholar
  15. Alexander J. Martin, “Hackers 'fool' iPhone X Face ID with a simple mask,” Sky News, 2017.Google ScholarGoogle Scholar
  16. Jukka Määttä, Abdenour Hadid, and Matti Pietikäinen., “Face spoofing detection from single images using micro-texture analysis,” In IEEE International Joint Conference on Biometrics, Washington DC, USA, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Theodoros Papatheodorou, and Daniel Rueckert, “3D face recognition,” In Face Recognition, InTech, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  18. Liangliang Shi, Xia Wang, and Yongliang She, “Research on 3D face recognition method based on LBP and SVM,” Optik 2020, 165157.Google ScholarGoogle Scholar
  19. Shalini Gupta, Kenneth R. Castleman, Mia K. Markey, and Alan C. Bovik, “Texas 3D face recognition database,” In IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI), 2010.Google ScholarGoogle ScholarCross RefCross Ref
  20. Yinjie Lei, Yulan Guo, Munawar Hayat, Mohammed Bennamoun, and Xinzhi Zhou, “A two-phase weighted collaborative representation for 3D partial face recognition with single sample,” Pattern Recognition 2016; 52:218–237.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Huibin Li, Di Huang, Jean-Marie Morvan, Yunhong Wang, and Liming Chen, “Towards 3D face recognition in the real: a registration-free approach using fine-grained matching of 3D keypoint descriptors,” International Journal of Computer Vision 2015; 113(2):128–142Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Luuk Spreeuwers, “Fast and accurate 3d face recognition,” International journal of computer vision 2011; 93(3):389–414.Google ScholarGoogle Scholar
  23. Baris Gecer, Stylianos Ploumpis, Irene Kotsia, and Stefanos Zafeiriou, “Generative adversarial network fitting for high fidelity 3d face reconstruction,” In IEEE Conference on Computer Vision and Pattern Recognition 2019 (pp. 1155–1164).Google ScholarGoogle Scholar
  24. Ying Cai, Yinjie Lei, Menglong Yang, Zhisheng You, and Shiguang Shan, “A fast and robust 3D face recognition approach based on deeply learned face representation,” Neurocomputing 2019; 363:375–397.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Guodong Guo, and Na Zhang, “A survey on deep learning based face recognition,” Computer Vision and Image Understanding 2019; 189:102805.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Timothy Faltemier, Kevin W. Bowyer, and Patrick J. Flynn, “Using a multi-instance enrollment representation to improve 3D face recognition,” In IEEE International Conference on Biometrics: Theory, Applications, and Systems 2007 (pp. 1–6).Google ScholarGoogle Scholar
  27. Xing Zhang, Lijun Yin, Jeffrey F.Cohn, Shaun Canavan, Michael Reale, Andy Horowitz, Peng Liu, and Jeffrey M.Girard, “Bp4d-spontaneous: a high-resolution spontaneous 3d dynamic facial expression database,” Image and Vision Computing 2014; 32, 692–706.Google ScholarGoogle Scholar
  28. Tinthid Jaikla, Sasakorn Pichetjamroen, Chalee Vorakulpipat, and Achara Pichetjamroen, “A Secure Four-factor Attendance System for Smartphone Device,” In International Conference on Advanced Communication Technology, PyeongChang, South Korea, 2020.Google ScholarGoogle ScholarCross RefCross Ref
  29. Mei Ngan, Patrick Grother, and Kayee Hanaoka, “Ongoing Face Recognition Vendor Test (FRVT): Part 6A: Face recognition accuracy with masks using pre-COVID-19 algorithms,” NIST Interagency/Internal Report (NISTIR 8311), 2020.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Other conferences
    ICMLSC '21: Proceedings of the 2021 5th International Conference on Machine Learning and Soft Computing
    January 2021
    178 pages
    ISBN:9781450387613
    DOI:10.1145/3453800

    Copyright © 2021 ACM

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    Publication History

    • Published: 18 June 2021

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