Attendance System Using Face Recognition

Authors

  • Ojus Arora  Department of Computer Engineering, Datta Meghe College of Engineering, Airoli, Navi Mumbai, Maharashtra, India
  • Ravi Purohit   Department of Computer Engineering, Datta Meghe College of Engineering, Airoli, Navi Mumbai, Maharashtra, India
  • Hemashree Samant  Department of Computer Engineering, Datta Meghe College of Engineering, Airoli, Navi Mumbai, Maharashtra, India
  • Dr. Archana Gulati  Department of Computer Engineering, Datta Meghe College of Engineering, Airoli, Navi Mumbai, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT206243

Keywords:

Human Face Recognition, 3D face recognition, CMOS, Support Vector Machine, Independent Component Analysis, LDA, PCA

Abstract

Authentication is one of the significant issues in the era of information system. Among other things, human face recognition is one of known techniques which can be used for user authentication. As an important branch of biometric verification, HFR (Human Face Recognition) has been widely used in many applications, such as video monitoring/surveillance system, human-computer interaction, door access control system and network security. Face Recognition begins with extracting the coordinates of features such as width of mouth, width of eyes, pupil, and compare the result with the measurements stored in the database and return the closest record (facial metrics). Nowadays, there are a lot of face recognition techniques and algorithms found and developed around the world. This system uses this face detection for attendance of students in a classroom. The traditional method of attendance requires more physical effort. It can be a little time consuming. Through this system, attendance can be handled without human intervention. Not only the attendance on a daily basis but a small report required to track student activity (total number of lectures attended and pertaining percentages can be displayed).

References

  1. Techopedia.com,"What is Facial Recognition? - Techopedia",2018
  2. Animetrics, "Face Recognition Applications"Archived from the original on 2008-07-13Retrieved 2008-06-04.
  3. Zhang, Jian, Yan, Ke, He, Zhen-Yu, and Xu, Yong "A Collaborative Linear Discriminative Representation Classification Method for Face Recognition”,2014, International Conference on Artificial Intelligence and Software Engineering (AISE2014)Lancaster, PA: DEStech Publications
  4. Consumer Reports ,"Facial Recognition: Who's Tracking You in Public?",2016
  5. Bramer, Max, “ Artificial Intelligence in Theory and Practice: IFIP 19th World Computer Congress”, 2006, Santigo Springer Science+Business Media
  6. de Leeuw, Karl Bergstra , “The History of Information Security: A Comprehensive Handbook”, pp264–265ISBN 9780444516084.
  7. ScienceDaily, “Mugspot Can Find A Face In The Crowd -- Face-Recognition Software Prepares To Go To Work In The Streets",1997.
  8. Williams, Mark, "Better Face-Recognition Software", 2008
  9. RKimmel and GSapiro ,"The Mathematics of Face Recognition", 2007, SIAM News "Face Homepage"nist.gov.
  10. Crawford, Mark, "Facial recognition progress report",2011 SPIE Newsroom is hwarya Admane, Afrin Sheikh, Sneha Paunikar, Shruti Jawade, Shubhangi Wadbude, ProfMJSawarkar,Z, “A Review on Different Face Recognition Techniques”,2019,International Journal of scientific research in Computer science, Engineering and Information Technology
  11. Siswanto, ARS., Nugroho, AS., & Galinium, M,”Implementation of face recognition algorithm for biometrics based time attendance system”, 2014, International Conference on ICT For Smart Society (ICISS)
  12. Lu, J., & Plataniotis, KN.“Conversion from color to gray-scale images for face detection”,2009, IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  13. R Rahim ,” Research of Face Recognition with Fisher Linear Discriminant” ,2018, IOP MaterSciEng300 012037
  14. Pa RIbrahim and ZMZin, “Study of automated face recognition system for office door access control application,”2011, ProcIEEE IntConfCommunicationSoftwareNetwork.
  15. ChrisXiao, xuanLu, Xuan Kan, Bowmen Deu, Changhao chen, Hongkai Wen, Andrew Markhem, Niki Trigoni, Jon A Stankovic, “Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues”,2019, Proceedings of the 2019 World Wide Web Conference (WWW), San Francisco, CA, USA.ACM,NewYork,NY,USA.
  16. Face recognition with Python (https://realpython.com/face-recognition-with-python)
  17. Chaitanya P , Smitha Bhat, Sneha R, Swati K.S, “Automatic student attendance system using face recognition”,2016, Karnataka State Council for Science and Technology (KSCST), Banglore

Downloads

Published

2020-04-30

Issue

Section

Research Articles

How to Cite

[1]
Ojus Arora, Ravi Purohit , Hemashree Samant, Dr. Archana Gulati, " Attendance System Using Face Recognition , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.164-171, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT206243