Skip to main content
SpringerLink
Log in

Development and Simulation Analysis of a Robust Face Recognition Based Smart Locking System

  • Conference paper
  • First Online:
Innovations in Electronics and Communication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 33))

Abstract

Face recognition based smart locking systems are susceptible to variation in ambient light conditions. This paper presents a robust face recognition based smart locking system. The novelty of our work is that the choice of the algorithm for face detection and recognition is based on the intensity of light at that time. This system uses basic principal component analysis, linear discriminant analysis, and its variants for face detection and recognition. Access is granted to the user if their image matches one in a predefined database. In case the light intensity is so low that no algorithm gives a satisfactory result, our system will authenticate via a Bluetooth-based one-time passcode. MATLAB/Simulink was used to simulate this system which was subsequently prototyped. The developed prototype had 90% accuracy in low light conditions when larger training databases are used and 90% accuracy in normal light conditions when smaller training databases are used.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Duino J (2015) Trusted face in lollipop, explained, In: Android central. Available via http://www.androidcentral.com/face-unlock-explained. Accessed 27 Jan 2017

  2. Hard A (2016) The 10 most expensive cars in the world make Teslas look like toyotas. In: Digitaltrends. Available via http://The10MostExpensiveCarsintheWorldMakeTeslaslooklikeToyotas. Accessed 27 Mar 2017

    Google Scholar 

  3. National Crime Records Bureau, Crime in India: a compendium, Ministry of Home Affairs, Government of India, New Delhi, 2014

    Google Scholar 

  4. MSN (2016) 25 companies richer than countries. In: MSN. Available via http://www.msn.com/en-za/money/financephotos/25-companies-richer-than-countries/ss-BBrvrKF#image=4. Accessed 27 Mar 2017

  5. Wang H, Li S, Wang Y (2017) Face recognition under varying lighting conditions using self-quotient image. In Proceedings of the sixth IEEE international conference on automatic face and gesture recognition, Seoul

    Google Scholar 

  6. Ding C, Choi J, Tao D, Davis L (2016) Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans Pattern Anal Mach Intell 38(3):518–531

    Article  Google Scholar 

  7. Li B, Mian A, Liu W (2017) Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE workshop on applications of computer vision (WACV), Tampa, pp 186–192

    Google Scholar 

  8. Lai Z, Dai D, Ren C, Huang K (2014) Multilayer surface Albedo for face recognition with reference images in bad lighting conditions. IEEE Trans Image Process 23(11):4709–4723

    Article  MathSciNet  Google Scholar 

  9. Liu H, Yang M, Gao Y, Cui C (2014) Local histogram specification for face recognition under varying lighting conditions. Image Vis Comput 32(5):335–347

    Article  Google Scholar 

  10. Turk M, Pentland A (1997) Face recognition using eigenfaces. In: Proceedings of the 1991 IEEE computer society conference on computer vision and pattern recognition, Maui, pp 586–591

    Google Scholar 

  11. Sinha U, Kangarloo H (2002) Principal component analysis for content-based image retrieval. RadioGraphics 22(5):1271–1289

    Article  Google Scholar 

  12. Izenman A (2013) Linear discriminant analysis. In: Modern multivariate statistical techniques, pp 237–280

    Google Scholar 

  13. Wagh P, Thakare R, Chaudhari J (2015) Attendance system based on face recognition using Eigen face and PCA algorithms. In: 2015 international conference on green computing and internet of things (ICGCIoT), Noida, pp 303–308

    Google Scholar 

  14. Sahani M, Nanda C, Sahu A (2015) Web-based online embedded door access control and home security system based on face recognition. In: 2015 international conference on circuit, power and computing technologies (ICCPCT), Nagercoil, pp 1–6

    Google Scholar 

  15. Faisal M, Thakur A (2017) Autonomous car system using facial recognition and geo location services. In: 2016 6th international conference on cloud system and big data engineering (confluence), IEEE, pp 417–420

    Google Scholar 

  16. Khowaja S, Dahri K, Khumbar M (2015) Facial expression recognition using two-tier classification and its application to smart home automation system. In 2015 international conference on emerging technologies (ICET), Peshawar, pp 1–6

    Google Scholar 

  17. Dhere P (2015) Review of PCA, LDA and LBP algorithms used for 3D Face Recognition. Int J Eng Sci Innovative Technol (IJESIT) 4(1):375–378

    Google Scholar 

  18. Singh A (2012) Comparison of face recognition algorithms on dummy faces. The Int J Multimedia its Appl 4(4):121–135

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru, India

    D. Sagar & Murthy K. R. Narasimha

Authors
  1. D. Sagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Murthy K. R. Narasimha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Sagar .

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    Ravi Kishore Singh

  3. Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA

    Vishal M. Patel

  4. Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    K. Santhi

  5. Research and Development, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    S.V. Ranganayakulu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sagar, D., Narasimha, M.K.R. (2019). Development and Simulation Analysis of a Robust Face Recognition Based Smart Locking System. In: Saini, H., Singh, R., Patel, V., Santhi, K., Ranganayakulu, S. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 33. Springer, Singapore. https://doi.org/10.1007/978-981-10-8204-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8204-7_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8203-0

  • Online ISBN: 978-981-10-8204-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation