Skip to main content

Real-Time Access Control System Method Using Face Recognition

  • Conference paper
  • First Online:
Proceedings of International Conference on Smart Computing and Cyber Security (SMARTCYBER 2020)

Abstract

Face recognition has been widely studied and studied for many years, but most PC-based face recognition systems have very limited portability and mobility. Face recognition is a process of dynamically capturing facial features through a camera connected to a computer and simultaneously comparing the captured facial features with the facial features previously entered into the personnel library. Face recognition-based person authentication system has been popular among other biometrics recently. This technology can be applied to important departments such as public security, banking, and customs to provide convenient and efficient detection methods. In this paper, we discuss a method of access control system using face recognition technology for entrance limited places. Some face recognition technologies that have shown state-of-the-art performance at their time are discussed. Here we present a method for access control system using facial recognition technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. B. Amos, B. Ludwiczuk, M. Satyanarayanan, OpenFace: A General-purpose Face Recognition Library with Mobile Applications. Technical report, CMU-CS-16-118, CMU School of Computer Science (2016)

    Google Scholar 

  2. F. Schroff, D. Kalenichenko, J. Philbin, Facenet: A Unified Embedding for Face Recognition and Clustering. arXiv preprint arXiv:1503.03832 (2015)

    Google Scholar 

  3. J. Deng, J. Guo, S. Zafeiriou, ArcFace: Additive Angular Margin Loss for Deep Face Recognition. arXiv:1801.07698 (2018)

    Google Scholar 

  4. H. Wang, Y. Wang, Z. Zhou, X. Ji, Z. Li, D. Gong, J. Zhou, W. Liu, Cosface: large margin cosine loss for deep face recognition, in CVPR (2018)

    Google Scholar 

  5. D. Yi, Z. Lei, S. Liao, S. Z. Li, Learning Face Representation from Scratch. arXiv preprint ss (2014)

    Google Scholar 

  6. H.-W. Ng, S. Winkler, A data-driven approach to cleaning large face datasets, in Proceedings of the ICIP (2014). https://vinstage.winklerbros.net/facescrub.html

  7. A. Okumura, T. Hoshino, S. Hada, Yugo Nishiyama, M. Tabuchi, Identity verification of ticket holders at large-scale events using face recognition. J. Inf. Process. 25, 448–458 (2017)

    Google Scholar 

  8. P. Li, C. Cadell, Chine Eyes ‘Black Tech’ to Boost Security as Parliament Meets. (March 10, 2018) Retrieved April 4, 2019 from Technology News from Reuters website: https://www.reuters.com/article/us-china-parliament-surveillance/china-eyes-black-tech-to-boost-security-as-parliament-meets-idUSKBN1GM06M?utm_campaign=trueAnthem:+Trending+Content&utm_content=5aa3f9fd04d30121e40e5e73&utm_medium=trueAnthem&utm_source=twitter

  9. G. Fleishman, Face ID on the iPhone X: Everything you Need to Know About Apple’s Facial Recognition, (2017, December 1) Retrieved December 4, 2017 from the Macworld from IDG website: https://www.macworld.com/article/3225406/iphone-ipad/face-id-iphone-x-faq.html

  10. S. Chopra, R. Hadsell, Y. LeCun, Learning a similarity metric discriminatively, with application to face verification, in Conference on Computer Vision and Pattern Recognition (CVPR) (2005)

    Google Scholar 

  11. E. Hoffer, N. Ailon, Deep metric learning using triplet network, in International Workshop on Similarity-Based Pattern Recognition (2015)

    Google Scholar 

  12. Y. Wen, K. Zhang, Z. Li, Y. Qiao, A discriminative feature learning approach for deep face recognition, in European Conference on Computer Vision (ECCV) (2016), pp. 499–515

    Google Scholar 

  13. W. Liu, Y. Wen, Z. Yu, M. Yang, Large-margin softmax loss for convolutional neural networks, in International Conference on Machine Learning (ICML) (2016)

    Google Scholar 

  14. W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, L. Song, SphereFace: deep hypersphere embedding for face recognition, in Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  15. Global Times, AI, Robots Help Provide Security for SCO Summit in Qingdao. Retrieved April 4ss, 2019 from Global Times website: https://www.globaltimes.cn/content/1106310.shtmla

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ahmed Abdulhakim Al-Absi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Al-Absi, M.A., Tolendiyev, G., Lee, H.J., Al-Absi, A.A. (2021). Real-Time Access Control System Method Using Face Recognition. In: Pattnaik, P.K., Sain, M., Al-Absi, A.A., Kumar, P. (eds) Proceedings of International Conference on Smart Computing and Cyber Security. SMARTCYBER 2020. Lecture Notes in Networks and Systems, vol 149. Springer, Singapore. https://doi.org/10.1007/978-981-15-7990-5_9

Download citation

Publish with us

Policies and ethics