Abstract
Computer Vision is considered as the science and technology of the machines that see. When paired with deep learning, it has limitless applications in various fields. Among various applications, face recognition is one of the most useful real-life problem-solving applications. We propose a technique that uses image enhancement and facial recognition technique to develop an innovative and time-saving class attendance system. The idea is to train a Convolutional Neural Network (CNN) using the enhanced images of the students in a certain course and then using that learned model, to recognize multiple students present in a lecture. We propose the use of deep learning model that is provided by OpenFace to train and recognize the images. This proposed solution can be easily installed in any organization, if the images of all persons to be marked this way are available with the administration. The proposed system marks attendance of students 100% accurately when captured images have faces in right pose and are not occluded.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Islam, M.M., Hasan, M.K., Billah, M.M., Uddin, M.M.: Development of smartphone based student attendance system. In: 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 230–233, Dhaka, (2017). https://doi.org/10.1109/r10-htc.2017.8288945
Adal, H., Promy, N., Srabanti, S., Rahman, M.: Android based advanced attendance vigilance system using wireless network with fusion of bio-metric fingerprint authentication. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp. 217–222, Chuncheon-si Gangwon-do, Korea (South), (2018). https://doi.org/10.23919/icact.2018.8323702
Charity, A., Okokpujie, K., Etinosa, N.O.: A bimodal biometrie student attendance system. In: 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), pp. 464–471, Owerri (2017). https://doi.org/10.1109/nigercon.2017.8281916
Okokpujie, K., Noma-Osaghae, E., John, S., Grace, K.A., Okokpujie, I.: A face recognition attendance system with GSM notification. In: 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), pp. 239–244, Owerri (2017). https://doi.org/10.1109/nigercon.2017.8281895
Poornima, S., Sripriya, N., Vijayalakshmi, B., Vishnupriya, P.: Attendance monitoring system using facial recognition with audio output and gender classification. In: 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), pp. 1–5, Chennai (2017). https://doi.org/10.1109/icccsp.2017.7944103
Lukas, S., Mitra, A.R., Desanti, R.I., Krisnadi, D.: Student attendance system in classroom using face recognition technique. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1032–1035, Jeju (2016). https://doi.org/10.1109/ictc.2016.7763360
Rekha, E., Ramaprasad, P.: An efficient automated attendance management system based on Eigen face recognition. In: 2017 7th International Conference on Cloud Computing, Data Science Engineering—Confluence, pp. 605–608, Noida, (2017). https://doi.org/10.1109/confluence.2017.7943223
Chintalapati, S., Raghunadh, M.V.: Automated attendance management system based on face recognition algorithms. In: 2013 IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–5, Enathi (2013). https://doi.org/10.1109/iccic.2013.6724266
Kurniawan, V., Wicaksana, A., Prasetiyowati, M.I.: The implementation of eigenface algorithm for face recognition in attendance system. In: 2017 4th International Conference on New Media Studies (CONMEDIA), pp. 118–124, Yogyakarta (2017). https://doi.org/10.1109/conmedia.2017.8266042
Raghuwanshi, A., Swami, P.D.: An automated classroom attendance system using video based face recognition. In: 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), pp. 719–724, Bangalore (2017). https://doi.org/10.1109/rteict.2017.8256691
Amos, B., Ludwiczuk, B., Satyanarayanan, M.: OpenFace: a generalpurpose face recognition library with mobile applications. CMU-CS-16-118, School of Computer Science, Carnegie Mellon University (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823, Boston, MA (2015). https://doi.org/10.1109/cvpr.2015.7298682
Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. (239) (2014)
Django (Version 2.0.1) [Computer Software] (2018). https://djangoproject.com
Subramanyam, B., Joshi, P., Meena, M.K., Prakash, S.: Quality based classification of images for illumination invariant face recognition. In: 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1–6, Sendai (2016). https://doi.org/10.1109/isba.2016.7477245
Acknowledgements
Authors are thankful to the volunteers who willingly gave their images for this study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Agrawal, A., Garg, M., Prakash, S., Joshi, P., Srivastava, A.M. (2020). Hand Down, Face Up: Innovative Mobile Attendance System Using Face Recognition Deep Learning. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_29
Download citation
DOI: https://doi.org/10.1007/978-981-32-9291-8_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9290-1
Online ISBN: 978-981-32-9291-8
eBook Packages: EngineeringEngineering (R0)