Implementation of Smart Attendance System Using Raspberry Pi

Authors

  • Rashmi R. Rumane  M.E Student, Department of Electronics & Communication Engineering, Sipna College of Engineering and Technology, Amravati, Maharashtra, India
  • Dr. Ajay. A. Gurjar  Professor, Department of Electronics & Communication Engineering, Sipna College of Engineering and Technology, Amravati, Maharashtra, India

Keywords:

Raspberry pi, Face Detection, Face Recognition, Attendance System, Linux (Python)

Abstract

Being one of the most successful applications of the image processing, face detection and recognition has a vital role in technical field especially in the field of security purpose. Human face detection and recognition is an important field for verification purpose especially in the case of attendance system. Maintaining the attendance is very important in all the institutes for checking the presence of students. Every institute has its own method in this regard. Some are taking attendance manually using the traditional pen and paper or file based approach. This system is developed for deploying an easy and a secure way of taking down attendance. The system first captures an image of all the students and stores the information into database. The system then stores the image by mapping it into a face coordinate structure. Next time whenever the registered student enters the premises the system recognizes the student and marks his attendance along with the time. In this project, we come up with a new hardware system for human face detection which makes use of Raspberry Pi. It is a credit-card sized computer with the components mounted on a credit card sized motherboard, running a dedicated version of Linux. It plugs into TV and a keyboard. It is a capable little computer which can be used in electronic devices and for much functionality that a desktop computer can perform. It comes at a very low price

References

  1. M. Molina Shamanth G S ashwin Kashyap ‘‘face detection using Raspberry pi and python,’’ .NCPD 2016 July 2016.
  2. S. C. Gaddam, N. V. K. Ramesh and Hema Dsn
  3. hanekula,’’ Face Recognition Based Attendance Management System with Raspberry PI 2 using Eigen Faces Algorithm, ’’ARPN journal vol -11, NO.13, july 2016.
  4. Raspberry Pi Face Recognition Treasure Box Created by Tony Di Cola.
  5. MedakTeenaRavali, Prof. RangasaiKomaragiri “Image Processing Platform On Raspberry Pi For Face Recognition” Global Journal of Advanced Technologies, ISSN 2277-6370 Vol3, Issue4- 2014.
  6. R.Pahune ,A.A.Chaudhuri,”Face Detection System for Security Purpose Using Raspberry PI,”ICEIS-2016.
  7. Richard Mo Adnan Shaout, ’’Portable Facial Recognition Jukebox using Fisher Faces, ’’IJACSA Vol.7, No.3, 2016.
  8. Subhanishaik, Antomicheal,’’Automatic Age and Gender Recognition in Human face Image Data Set Using Convolutional Neural Network System,’’IJARCSMS Vol 4, issue 2, Feb 2016 .ISSN:2321-7782.
  9. Danwei, ’’Distributed Compressive Sensing Based Near Infrared and Visible Images Fusion for Face Recognition,’’ IJSP, IPPR vol.9, No.4 (2016), pp.281-292.
  10. Unsoojang and Eui Chul lee, ’’Performance Analysis of Pixel Based Face Recognition Method, ’’IJBSBT Vol.8, No. 2(2016), PP.197-206.
  11. Li yongqiang and panjin, ‘‘one sample image Recognition algorithm based on improved sub-pattern principle component analysis, ’’IJSP,IPPR vol.8,NO.9(2015),PP.77-84.
  12. Maryam Moghaddam, Saeed Meshgini,”Automatic face Recognition via Local Directional Patterns,” journal of Artificial Intelligence in Electrical Engineering, vol.4, NO.15, DEC 2015.
  13. Navin Prakash, Dr. Y. singh, ’’Support vector Machines for Face Recognition ’’IRJET Vol: 02 issue: 08|Nov-2015.
  14. JunLee, Jeong-Sik Park, Gil-Jin Jang and Yong-Hoseo,’’ Efficient Head Pose Determination and its Application to face Recognition on Multi-Pose face DB,’’IJMUE Vol.11.No2 (2016).
  15. G. Senthilkumar, K. Gopalakrishnan, V.S.Kumar,’’ Embedded Image Capturing System Using Raspberry PI System,’’ IJETTCS Volume 3, Issue 2, March-April 2014.
  16. Gheorghita Ghinea ,Rajkumar Kannan and Suresh Kannaiyan, ”Gradient orientation-based PCA Subspace for Novel Face Recognition ”in IEEE Access, Vol 2,2169-3536 2014.
  17. Mounika B.R Reddy N. Jand Reddy V.B.D,’’A Neural Network Based Face Detection Using Gabor Filter Response, ’’IJNN ISSN:2249 -2763 &ESSN: 2249-2771, volume 2 ,Issue 1, 2012 ,PP.-06-09.
  18. Tudor Barbu,’’Gabor Filter –Based Face Recognition Technique, ’’Processing of theDomain Academy, Series A, Vol 11, No 31 2010,PP.277-283.
  19. H.R.Kanan and K.Faez,’’Adaptively Weighted Sub –Gabor Array for Single Model –based human face Recognition’’. IJECE DEC 2010.
  20. P. Shih and, C. Liu, ’’Face Detection using Discriminating Feature Analysis and Support Vector Machine, ’’.Pattern Recognition Vol.39, NO.2, Feb 2006, PP.260.276.
  21. S.K.Nabeena. N. V. Narayana Rao,’’ Implementation of Video Surveillance Using Raspberry PI 2,’’ IJMETMR ISSN No:2348-4845.
  22. Sumo .M.O, Rashmi .H.N, S.B.Seshadri.’’ Stand Alone Face Recognition System Using Principle Component Analysis,’’ IJETCAS.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2018-04-30

Issue

Volume 4, Issue 5, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Rashmi R. Rumane, Dr. Ajay. A. Gurjar, " Implementation of Smart Attendance System Using Raspberry Pi , International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 5, pp.1650-1658, March-April-2018.