SpringerLink
Log in

Integrating State-of-the-Art Face Recognition and Anti-Spoofing Techniques into Enterprise Information Systems

  • Conference paper
  • First Online:
Artificial Intelligence and Mobile Services – AIMS 2023 (AIMS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14202))

Included in the following conference series:

Abstract

Face Recognition Technology and Face Anti-Spoofing became a necessity during the Covid 19 pandemic, Monkeypox Virus etc. In the current era, the use of contactless technology has become crucial and highly beneficial for individuals. Vietnam is experiencing a significant digital transformation across various sectors including culture, education, tourism, finance, industry, and entertainment. However, most Enterprise Information System institutions in Vietnam lack facial recognition. To address this issue, we have undertaken research to devise a secure anti-spoofing method and determine an effective approach for face recognition processing. Our aim is to develop a comprehensive solution that can be implemented to establish a complete system that we researched and assessed at each stage. To construct a Facial Recognition application, we implemented a Convolutional Neural Network (CNN) as a core to recognize faces in real-time. To identify whether the faces are genuine or counterfeit, we utilized Landmark68 during the anti-spoofing phase. We applied our findings and developed an application AILib during the Covid-19 outbreak, when it was challenging for people to physically visit and login with their IDs at the counter. People can now login without physically being there by logging in using their faces on the AILib. According to the findings of our research, the system functions satisfactorily, with an ideal level of accuracy of 98.42%. Furthermore, we discovered that the optimal threshold value for identifying Asian faces in our face recognition test was determined to be 0.4, while varying threshold values were determined for different face types. For anti-spoofing, during the facial anti-spoofing test, the best threshold value for left, right and front was d < −50, d < −150 and d > −50 respectively, and the right value is d > −50 and in comparison, with state-of-the-art methods is pretty good. The program has a high level of practicality, significantly advancing the groundbreaking application of artificial intelligence to enhance people’s quality of life and safety.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 42.79
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 53.49
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lin, S.-H.: An introduction to face recognition technology. Inf. Sci. Int. J. Emerg. Transdiscipl. 3, 1–7 (2000)

    Google Scholar 

  2. Berle, I.: What is face recognition technology? In: Berle, I. (ed.) Face Recognition Technology: Compulsory Visibility and Its Impact on Privacy and the Confidentiality of Personal Identifiable Images, pp. 9–25. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36887-6_2

  3. Manjula, V., Baboo, L.: Face detection identification and tracking by PRDIT algorithm using image database for crime investigation. Int. J. Comput. Appl. 38, 40–46 (2012). https://doi.org/10.5120/4741-6649

    Article  Google Scholar 

  4. Lander, K., Bruce, V., Bindemann, M.: Use-inspired basic research on individual differences in face identification: implications for criminal investigation and security. Cogn. Res Princ. Implic. 3, 26 (2018). https://doi.org/10.1186/s41235-018-0115-6

    Article  Google Scholar 

  5. Hu, Y., An, H., Guo, Y., Zhang, C., Zhang, T., Ye, L.: The development status and prospects on the face recognition. In: 2010 4th International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4 (2010). https://doi.org/10.1109/ICBBE.2010.5517197

  6. Mishra, S., Vi, P.T., Phuc, V.M., Oni, D., Tanh, N.V.: Using security metrics to determine security program effectiveness. In: Human Factors in Cybersecurity. AHFE Open Acces (2023). https://doi.org/10.54941/ahfe1003720

  7. Mishra, S., Phuc, V.M., Tanh, N.V.: Lightweight authentication encryption to improve DTLS, quark combined with overhearing to prevent DoS and MITM on low-resource IoT devices. In: Tekinerdogan, B., Wang, Y., Zhang, L.-J. (eds.) ICIOT 2022. LNCS, vol. 13735, pp. 108–122. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-23582-5_8

  8. Mishra, S., Phuc, V.M., Igbagbo, O.D.: BNIS- Bot Node Isolation Strategy to Prevent DoS Attacks: An Improved Overhearing Solution

    Google Scholar 

  9. Mishra, S., Thanh, L.T.: SATMeas - object detection and measurement: canny edge detection algorithm. In: Pan, X., **, T., Zhang, L.-J. (eds.) AIMS 2022. LNCS, vol. 13729, pp. 91–101. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23504-7_7

  10. Hoyle, D.C., Rattray, M.: PCA learning for sparse high-dimensional data. Europhys. Lett. (EPL) 62, 117–123 (2003). https://doi.org/10.1209/epl/i2003-00370-1

    Article  Google Scholar 

  11. Vijay, K., Selvakumar, K.: Brain FMRI clustering using interaction K-means algorithm with PCA. In: 2015 International Conference on Communications and Signal Processing (ICCSP), pp. 0909–0913 (2015). https://doi.org/10.1109/ICCSP.2015.7322628

  12. Vogt, F., Mizaikoff, B., Tacke, M.: Numerical methods for accelerating the PCA of large data sets applied to hyperspectral imaging. Presented at the Proceedings of the SPIE, 22 February (2002). https://doi.org/10.1117/12.456960

  13. Ordonez, C., Mohanam, N., Garcia-Alvarado, C.: PCA for large data sets with parallel data summarization. Distrib. Parallel Databases 32, 377–403 (2013). https://doi.org/10.1007/s10619-013-7134-6

    Article  Google Scholar 

  14. Wang, W., Yang, J., **ao, J., Li, S., Zhou, D.: Face Recognition Based on Deep Learning. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds.) HCC 2014. LNCS, vol. 8944, pp. 812–820. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15554-8_73

    Chapter  Google Scholar 

  15. Li, Y., Cha, S.: Implementation of robust face recognition system using live video feed based on CNN. ar**v preprint ar**v:1811.07339 (2018)

  16. Schuckers, S.A.: Spoofing and anti-spoofing measures. Inf. Secur. Tech. Rep. 7, 56–62 (2002)

    Article  Google Scholar 

  17. Chakka, M.M. (eds.): Competition on Counter Measures to 2-D Facial Spoofing Attacks (2011). https://doi.org/10.1109/IJCB.2011.6117509

  18. Chetty, G., Wagner, M.: Liveness verification in audio-video speaker authentication. In: Proceedings of the 10th ASSTA Conference, pp. 358–363. Macquarie University Press

    Google Scholar 

  19. Frischholz, R., Dieckmann, U.: BioID: a multimodal biometric identification system. Computer 33, 64–68 (2000). https://doi.org/10.1109/2.820041

    Article  Google Scholar 

  20. Kollreider, K., Fronthaler, H., Faraj, M.I., Bigun, J.: Real-time face detection and motion analysis with application in “liveness” assessment. IEEE Trans. Inf. Forensics Secur. 2, 548–558 (2007). https://doi.org/10.1109/TIFS.2007.902037

    Article  Google Scholar 

  21. De Marsico, M., Nappi, M., Riccio, D., Dugelay, J.-L.: Moving face spoofing detection via 3D projective invariants. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 73–78 (2012). https://doi.org/10.1109/ICB.2012.6199761

  22. Pavlidis, I., Symosek, P.: The imaging issue in an automatic face/disguise detection system. In: Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640), pp. 15–24 (2000). https://doi.org/10.1109/CVBVS.2000.855246

  23. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57, 137–154 (2004)

    Article  Google Scholar 

  24. Coronavirus disease (COVID-19) – World Health Organization. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. Accessed 12 Aug 2022

  25. Mishra, S., Minh, C.S., Thi Chuc, H., Long, T.V., Nguyen, T.T.: Automated robot (car) using artificial intelligence. In: 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), pp. 319–324 (2022). https://doi.org/10.1109/ISMODE53584.2022.9743130

  26. Monkeypox cases are rising—here’s what we know so far. https://www.nationalgeographic.com/science/article/monkeypox-cases-are-risingheres-what-we-know-so-far. Accessed 12 Aug 2022

  27. Norstrom, P., Consulting, A.: Has Covid increased public faith in facial recognition? Biometric Technol. Today 2021, 5–8 (2021). https://doi.org/10.1016/S0969-4765(21)00121-1

    Article  Google Scholar 

  28. This is the first university in Vietnam to take attendance with face recognition, evasion is only in the past. https://tipsmake.com/this-is-the-first-university-in-vietnam-to-take-attendance-with-face-recognition-evasion-is-only-in-the-past. Accessed 12 Aug 2022

  29. News, V.: Báo VietnamNet. https://vietnamnet.vn/en/vietnamese-engineers-develop-face-recognition-smart-lock-2132161.html. Accessed 29 June 2023

  30. Hu, P., Ramanan, D.: Finding Tiny Faces (2017). http://arxiv.org/abs/1612.04402

  31. Yap, J.W., bin Mohd Yussof, Z., bin Salim, S.I., Lim, K.C.: Fixed point implementation of Tiny-Yolo-v2 using OpenCL on FPGA. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 9 (2018). https://doi.org/10.14569/IJACSA.2018.091062

  32. Yao, G., Lei, T., Zhong, J.: A review of Convolutional-Neural-Network-based action recognition. Pattern Recogn. Lett. 118, 14–22 (2019). https://doi.org/10.1016/j.patrec.2018.05.018

    Article  Google Scholar 

  33. Dhillon, A., Verma, G.K.: Convolutional neural network: a review of models, methodologies and applications to object detection. Prog. Artif. Intell. 9, 85–112 (2020). https://doi.org/10.1007/s13748-019-00203-0

    Article  Google Scholar 

  34. Fang, W., Love, P.E.D., Luo, H., Ding, L.: Computer vision for behaviour-based safety in construction: a review and future directions. Adv. Eng. Inform. 43, 100980 (2020). https://doi.org/10.1016/j.aei.2019.100980

    Article  Google Scholar 

  35. Palaz, D., Magimai-Doss, M., Collobert, R.: End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition. Speech Commun. 108, 15–32 (2019). https://doi.org/10.1016/j.specom.2019.01.004

    Article  Google Scholar 

  36. Li, H.-C., Deng, Z.-Y., Chiang, H.-H.: Lightweight and resource-constrained learning network for face recognition with performance optimization. Sensors 20, 6114 (2020). https://doi.org/10.3390/s20216114

    Article  Google Scholar 

  37. Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning. https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78

    Google Scholar 

  38. face-api.js. https://justadudewhohacks.github.io/face-api.js/docs/index.html#models-face-landmark-detection. Accessed 13 Aug 2022

  39. Liberti, L., Lavor, C., Maculan, N., Mucherino, A.: Euclidean distance geometry and applications. SIAM Rev. 56, 3–69 (2014). https://doi.org/10.1137/120875909

    Article  MathSciNet  MATH  Google Scholar 

  40. How to Read Body Language and Facial Expressions. https://www.verywellmind.com/understand-body-language-and-facial-expressions-4147228. Accessed 13 Aug 2022

Download references

Author information

Authors and Affiliations

  1. International School, Vietnam National University, Hanoi, Vietnam

    Satyam Mishra & Cong-Doan Truong

  2. University of Science, Vietnam National University, Hanoi, Vietnam

    Nguyen Thi Bich Thuy

Authors
  1. Satyam Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nguyen Thi Bich Thuy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cong-Doan Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cong-Doan Truong .

Editor information

Editors and Affiliations

  1. Tsinghua University, Shenzhen, China

    Yujiu Yang

  2. University of Science and Technology, Bei**g, China

    **aohui Wang

  3. Shenzhen Entrepreneurship and Innovation Federation, Shenzhen, China

    Liang-Jie Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mishra, S., Thuy, N.T.B., Truong, CD. (2023). Integrating State-of-the-Art Face Recognition and Anti-Spoofing Techniques into Enterprise Information Systems. In: Yang, Y., Wang, X., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2023 . AIMS 2023. Lecture Notes in Computer Science, vol 14202. Springer, Cham. https://doi.org/10.1007/978-3-031-45140-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-45140-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45139-3

  • Online ISBN: 978-3-031-45140-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation