STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Electrical and Communication Engineering Technology Original Research

Facial Biometrics for Attendance System

  1. D.M.V. Priya Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A)
  2. P. Srinivasa Vinay Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A)
  3. K. Roopesh Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A)
  4. T. Prabhu Teja Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A)
  5. B. Jagadeesh Department of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses (A)

Abstract

This paper discusses the necessity for effective student attendance management systems by proposing a smart attendance management system utilizing biometric technology. It is well recognised that manually recording attendance is time-consuming and error prone. In this study, we propose a Facial Biometrics attendance system that automates attendance process without requiring human intervention. The system consists of a camera placed in the classroom to take pictures of the pupils. Subsequently, the captured images are analyzed for facial features through the implementation of the Histogram of Oriented Gradients (HOG) algorithm for precise face detection. After identification, the K-Nearest Neighbour (KNN) classifier is used to identify the faces. Then marked as present or absent upon the recognition results, and these detected facial records are securely stored within a database for future reference and analysis. The Technology allows Users to have access to real-time attendance data and can generate monthly attendance reports effortlessly. By employing this methodology, the system achieves efficiency and stability, resulting in a significant reduction in classroom attendance management costs

Keywords

References (17)

  1. Smitha, Pavithra S Hegde, Afshin, Yenepoya Institute of Technology. Face Recognition based Attendance Management System. International Journal of Engineering Research and. 2020;V9(05). doi:10.17577/ijertv9is050861
  2. Khan M, Chakraborty S, Astya R, Khepra S. Face detection and recognition using OpenCV. In: 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, October 18–19, 2019. pp. 116–119. doi:10.1109/ICCCIS48478. 2019.8974493.
  3. Gopila M, Prasad D. Machine learning classifier model for attendance management system. In: 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (ISMAC), Palladam, India, October 7–9, 2020. pp. 1034–1039. doi:10.1109/ISMAC49090. 2020.9243363.
  4. Bah SM, Ming F. An improved face recognition algorithm and its application in attendance management system. Array. 2020;5:100014. doi:10.1016/j.array.2019.100014
  5. Phatak SS, Patil HS, Arshad MW, Jitkar B, Patil S, Patil J. Advanced Face Detection using Machine Learning And AI-based Algorithm. 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). 2022:1111-1116. doi:10.1109/ic3i56241.2022.10072527
  6. OpenCY. OpenCV modules. [Online]. 2024. Available at https://docs.opencv.org/3.4/index.html
  7. Zeng W, Meng Q, Li R. Design of Intelligent Classroom Attendance System Based on Face Recognition. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 2019:611-615. doi:10.1109/itnec.2019.8729496
  8. Rojas Q. M, Masip D, Todorov A, Vitria J. Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE. 2011;6(8):e23323. doi:10.1371/journal.pone.0023323
  9. Histogram of Oriented Gradients of the face. [Online]. Available at https://www.researchgate.net/ figure/Histogram-of-Oriented-Gradients-of-the-face_fig4_51586877 [Accessed February 24, 2024].
  10. Abbas Helmi RA, Salsabil bin Eddy Yusuf S, Jamal A, Bin Abdullah MI. Face Recognition Automatic Class Attendance System (FRACAS). 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). 2019:50-55. doi:10.1109/i2cacis.2019.8825049
  11. SciKitLearn. scikit-learn: Machine learning in Python. [Online]. Available at https://scikitlearn.org/stable/
  12. Welcome to Read the Docs. [Online]. Available at https://facerecognition.readthedocs.io/en/latest/
  13. Python. Python 3.12.4 documentation. [Online]. Available at https://docs.python.org/3
  14. Jadhav A, Jadhav A, Ladhe T, Yeolekar K. Automated attendance system using face recognition. Int Res J Eng Technol. 2017; 4 (1): 1467–1471.
  15. Apoorva. P, Impana. HC, Siri. SL, Varshitha. MR, Ramesh. B. Automated Criminal Identification by Face Recognition using Open Computer Vision Classifiers. 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). 2019:775-778. doi:10.1109/iccmc.2019.8819850
  16. Chandran PS, Byju NB, Deepak RU, Nishakumari KN, Devanand P, Sasi PM. Missing Child Identification System Using Deep Learning and Multiclass SVM. 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS). 2018:113-116. doi:10.1109/raics.2018.8635054
  17. Rathi R, Choudhary M, Chandra B. An application of face recognition system using image processing and neural networks. Int J Computer Technol Appl. 2012; 3 (1): 45–49.