Abstract
In comparison to general manual operations, contemporary technology always saves time and is often more hassle-free when it comes to verifying human authenticity using their biometrical components. However, despite the fact that face recognition technology has been used in a variety of sectors such as human identification systems, this work is the first to describe how the Face Recognition Technique can be integrated with a deep learning approach. Advanced deep learning techniques can make the attendance system completely automated, highly secure, easier to use, and faster to implement than older systems. Nowadays, the Attendance System is becoming increasingly automated, resulting in time-saving, effective, and beneficial solutions that reduce the burden on administration and organizations. In this paper, we suggest an automatic attendance mechanism that is based on Deep Convolutional Neural Networks (DCNN). SeetaFace, a deep convolutional neural network-based face detection system, is employed in this research effort to detect faces in real-time video capture. This implementation is a VIPLFaceNet implementation, to be more specific. AlexNet, which is also a DCNN, is used for image categorization. The experimental results bring four short similarity situations of the classroom such as absence, delayed appearances, early leave, and unauthorized entry during class or session along with the name, student id, and section and passes this information to the attendance sheet which will evaluate the students/persons in the classroom. This methodology saves time when compared to the traditional method of attendance marking, as well as allows organizations to conduct stress-free observations of students and staff.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akay, E.O., Canbek, K.O., Oniz, Y.: Automated student attendance system using face recognition. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE (2020)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Darapaneni, N., Evoori, A.K., Vemuri, V.B., Arichandrapandian, T., Karthikeyan, G., Paduri, A.R., Babu, D., Madhavan, J.: Automatic face detection and recognition for attendance maintenance. In: 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS). IEEE (2020)
Ferdous, R.H., Arifeen, M.M., Eiko, T.S., Mamun, S.A.: Performance analysis of different loss function in face detection architectures. In: Advances in Intelligent Systems and Computing, pp. 659–669. Springer, Singapore (2021)
Filippidou, F.P., Papakostas, G.A.: Single sample face recognition using convolutional neural networks for automated attendance systems. In: 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS). IEEE (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016)
Kalaiarasi, P., Esther Rani, P.: A comparative analysis of AlexNet and GoogLeNet with a simple DCNN for face recognition. In: Advances in Intelligent Systems and Computing, pp. 655–668. Springer, Singapore (2021)
Ki Chan, C.C., Chen, C.C.: Continuous real-time automated attendance system using robust C2D-CNN. In: 202020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII). IEEE (2020)
Kumar, N., Madhavan, S.: Incremental weighted linear discriminant analysis for face recognition. In: Lecture Notes in Electrical Engineering, pp. 677–687. Springer, Singapore (2021)
Moshin Reza, S., Mahfujur Rahman, M., Parvez, H., Badreddin, O., Al Mamun, S.: Performance analysis of machine learning approaches in software complexity prediction. In: Advances in Intelligent Systems and Computing, pp. 27–39. Springer, Singapore (2021)
Qi, A., Wei, J., Bai, B.: Research on deep learning expression recognition algorithm based on multi-model fusion. In: 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE (2019)
Rahman, M.M., Mamun, S.A., Kaiser, M.S., Islam, M.S., Rahman, M.A.: Cascade classification of face liveliness detection using heart beat measurement. In: Advances in Intelligent Systems and Computing, pp. 581–590. Springer, Singapore (2021)
Rathod, H., Ware, Y., Sane, S., Raulo, S., Pakhare, V., Rizvi, I.A.: Automated attendance system using machine learning approach. In: 2017 International Conference on Nascent Technologies in Engineering (ICNTE). IEEE (2017)
Tabassum, T., Tasnim, N., Nizam, N., Al Mamun, S.: Anonymous person tracking across multiple camera using color histogram and body pose estimation. In: Advances in Intelligent Systems and Computing, pp. 639–648. Springer, Singapore (2021)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hasan, H.M., Rahman, M.M., Khan, M.AA., Meghla, T.I., Al Mamun, S., Kaiser, M.S. (2022). Implementation of Real-Time Automated Attendance System Using Deep Learning. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_10
Download citation
DOI: https://doi.org/10.1007/978-981-16-7597-3_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7596-6
Online ISBN: 978-981-16-7597-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)