Smart Attendance System using Face Recognition




Computer Vision, Machine Learning, Face Recognition, Open CV, Facial feature extraction



Face recognition offers a wide range of valuable applications in social media, security, and surveillance contexts. The software used for building facial recognition algorithms is Python and OpenCV. "Attendance using Face Recognition" is a method for tracking and managing attendance that makes use of facial recognition technology. By seamlessly integrating the 'Face Recognition' module, a native Python feature, and the OpenCV library, our system excels in accuracy and dependability. The system then stores attendance records in a database and provides real-time reports. In this article, we demonstrate how to create a face recognition system in Python utilizing the built-in "Face Recognition" module and the OpenCV library. Our results show that our system achieves high accuracy and robustness while being efficient and scalable, catering to a wide spectrum of educational institutions, organizations, and enterprises.


Hui J, Tang S, Hu S. Face Recognition Based on Convolutional Neural Networks and Softmax Regression. Proceedings of the 2017 International Conference on Image, Vision, and Computing (ICIVC), Chengdu, China. 2017; 112(116).

Lu J, Liong VE, Zhou J. Face Recognition using Local Quantized Patterns. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2012; Providence, RI, USA. p. 3562-3569.

Dey AC, Ghosh SB. Improving Attendance Management in Educational Institutions using Face Recognition. In: Proceedings of the International Conference on Machine Learning and Computer Vision; 2014. p. 345-350.

Blanz V, Vetter T. Face Recognition Based on Fitting a 3D Morphable Model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2003; 25(9):1063-1074.

Martinez AM, Benavente R. The AR face database. CVC Technical Report. 1998.

Deshmukh S, Rawat S. Face Recognition Technology. Journal of Intelligent Computing. 2023; 120:39-52.

Prathama V, Thippeswamy T. Age Invariant Face Recognition. Journal of Intelligent Computing. 2023; 120:53-68.

Mais Mohamed Husein, Alzubaydi D. Mobile Face Recognition Application using Eigen Face Approach for Android. Journal of Mobile Applications. 2021; 5(2):45-56.

Bhaskar J, Venkatesh V. Face Recognition for Attendance Management. Journal of Intelligent Computing. 2023; 120:25-38.

Malik U. Image Processing using OpenCV. Journal of Computer Vision and Image Processing. 2020; 3(1):15-28.

Bussa S, Mani A, Bharuka S. Smart Attendance System using OpenCV based on Face Recognition. Journal of Intelligent Computing. 2023; 120:83-96.

Minaee S, Abdolshah S, Khademi Kalantari N. Deep learning-based face recognition: A survey. Pattern Recognition. 2020; 107.

Ramya N, Manasa D, Naveed SK. Face Recognition for Automated Attendance Management. Journal of Intelligent Computing. 2023; 120:69-82.

Lyon DM, Fisher JW. Multimodal Person Recognition Using Unconstrained Audio and Video. Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition; 2002. p. 293-298.

Veluchamy S, Michael Mahesh K, Pon Bharathi A, Paul T Sheeba. DeepDrive: A braking decision-making approach using optimized GAN and Deep CNN for advanced driver assistance systems. Engineering Applications of Artificial Intelligence. 2023; 123(A):106111. ISSN 0952-1976.

Veluchamy S, Michael Mahesh K, Muthukrishnan R, Karthi S. HY-LSTM: A new time series deep learning architecture for estimation of pedestrian time to cross in advanced driver assistance system. Journal of Visual Communication and Image Representation. 2023; 97:103982. ISSN 1047-3203.




How to Cite

Viswanathan J, E K, S N, S V. Smart Attendance System using Face Recognition. EAI Endorsed Scal Inf Syst [Internet]. 2024 Feb. 26 [cited 2024 Apr. 20];. Available from:



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