In companies and educational institutions, attendance management is crucial. Traditional methods take a lot of time and are prone to proxy attendance. In order to identify people and automatically record attendance, this paper proposes an Automated Attendance System using Face Recognition that makes use of computer vision and machine learning. In order to update attendance records, the system takes real-time pictures, uses the Haar Cascade approach to identify faces, and then compares the photos with stored databases. Additionally, it creates attendance records and notifies parents and students via email and SMS. The technology reduces manual labor, increases accuracy, and is helpful in the prevention of false attendance.

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The following references provide the academic and technical foundation for the Automated Attendance System using Face Recognition, covering key concepts in face detection, recognition algorithms, deep learning, and image processing. [1]W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, Dec. 2003. [2]P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 511–518, 2001. [3]A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems (NIPS), vol. 25, pp. 1097–1105, 2012. [4]S. Z. Li and A. K. Jain, Eds., Handbook of Face Recognition, 2nd ed., ser. Advances in Computer Vision and Pattern Recognition. London, U.K.: Springer, pp. 1–35, 2011. [5]T. Kanade, “Picture Processing System by Computer Complex and Recognition of Human Faces,” U.S. Patent 3 990 007, Nov. 1976. [6]R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Upper Saddle River, NJ, USA: Pearson Education, pp. 120–150, 2018. [7]M. A. Turk and A. P. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.