IoT and Analytics for Sensor Networks pp 97–106 Cite as
Automatic Attendance Management System Using Face Detection and Face Recognition
- M. Varsha 12 &
- S. Chitra Nair 12
- Conference paper
- First Online: 12 September 2021
789 Accesses
1 Citations
Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 244))
Attendance plays a major role in every education system. Taking attendance of students manually can be a great burden for teachers. It may cause many problems like loss of time, repetition, incorrect markings, and difficulties in marking them. To avoid this, there is a need to design an automatic system that overcomes the issues with the traditional attendance system. There are many automatic methods available for this purpose like fingerprint systems, RFID systems, face recognition systems and iris recognition systems, etc. But among these Face Recognition proved to be more efficient. The main objective of this paper is to propose a model that captures images from videos, detect and recognize the faces, predict the recognized face, and then marks attendance. In this work, a basic step has been performed which uses fifteen classes from LFW Dataset and faces are detected, recognized, and then prediction is done on a randomly selected image from the used dataset. This system uses a combination of Multi Task Cascaded Neural Network (MTCNN) algorithm along with FaceNet that can be used to detect faces and extract facial features from images. SVM is used to predict the face of the person from the image. The proposed system obtains a classification accuracy of 99.177% for the training set and 100% on the test set. The accuracy, precision, recall, and F1-score are computed.
This is a preview of subscription content, log in via an institution .
Buying options
- Available as PDF
- Read on any device
- Instant download
- Own it forever
- Available as EPUB and PDF
- Compact, lightweight edition
- Dispatched in 3 to 5 business days
- Free shipping worldwide - see info
Tax calculation will be finalised at checkout
Purchases are for personal use only
Jayant, N. K., & Borra, S. (2016). Attendance management system using hybrid face recognition techniques. In 2016 Conference on advances in signal processing (CASP), June 9, pp. 412–417.
Google Scholar
Islam, M. S., Mahmud, A., Papeya, A. A., Onny, I. S., & Uddin, J. (2017). A combined feature extraction method for automated face recognition in classroom environment. In International symposium on signal processing and intelligent recognition systems, September, pp. 417–426.
Varadharajan, E., Dharani, R., Jeevitha, S., Kavinmathi, B., & Hemalatha, S. (2016). Automatic attendance management system using face detection. In 2016 Online international conference on green engineering and technologies (IC-GET) , November 19, pp. 1–3.
Arsenovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2017). FaceTime—Deep learning based face recognition attendance system. In 2017 IEEE 15th International symposium on intelligent systems and informatics (SISY) , September 14, pp. 000053–000058.
Hapani, S., Prabhu, N., Parakhiya, N., & Paghdal, M. (2018). Automated attendance system using image processing. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) , August 16, pp. 1–5.
Sanli, O., & Ilgen, B. (2018). Face detection and recognition for automatic attendance system. In Proceedings of SAI intelligent systems conference , September 6, pp. 237–245.
Sarkar, P. R., Mishra, D., & Subhramanyam, G. R. S. (2019). Automatic attendance system using deep learning framework. In Machine intelligence and signal analysis , pp. 335–346.
Gupta, R. K., Lakhlani, S., Khedawala, Z., Chudasama, V., Upla, K. P. (2018). A deep learning paradigm for automated face attendance. In Workshop on computer vision applications , December 18, pp. 39–50.
Nyein, T., Oo, A. N. (2019). University classroom attendance system using facenet and support vector machine. In 2019 International conference on advanced information technologies (ICAIT), November 6, pp. 171–176.
Brownlee, J. (2020). Machine learning algorithms in python, machine learning mastery. https://machinelearningmastery.com/machine-learning-with-python/ . Accessed 8 June 2020.
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE signal processing letters , 23 (10), 1499–1503.
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition; pp. 815–823.
Huang, G. B., Mattar, M., Berg, T., Learned-Miller, E. (2008). Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in ‘Real-Life’ Images: detection, alignment, and recognition , October.
Download references
Author information
Authors and affiliations.
NSS College of Engineering, Palakkad, India
M. Varsha & S. Chitra Nair
You can also search for this author in PubMed Google Scholar
Editor information
Editors and affiliations.
Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India
Padmalaya Nayak
Department of Computer Science and Engineering, Global Institute of Management and Technology, Nadia, West Bengal, India
Department of Creative Technologies and Product Design, National Taipei University of Business, Taipei City, T´ai-pei, Taiwan
Sheng-Lung Peng
Rights and permissions
Reprints and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper.
Varsha, M., Chitra Nair, S. (2022). Automatic Attendance Management System Using Face Detection and Face Recognition. In: Nayak, P., Pal, S., Peng, SL. (eds) IoT and Analytics for Sensor Networks. Lecture Notes in Networks and Systems, vol 244. Springer, Singapore. https://doi.org/10.1007/978-981-16-2919-8_9
Download citation
DOI : https://doi.org/10.1007/978-981-16-2919-8_9
Published : 12 September 2021
Publisher Name : Springer, Singapore
Print ISBN : 978-981-16-2918-1
Online ISBN : 978-981-16-2919-8
eBook Packages : Engineering Engineering (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
- Publish with us
Policies and ethics
- Find a journal
- Track your research
A review paper on attendance marking system based on face recognition
Ieee account.
- Change Username/Password
- Update Address
Purchase Details
- Payment Options
- Order History
- View Purchased Documents
Profile Information
- Communications Preferences
- Profession and Education
- Technical Interests
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
IMAGES
VIDEO
COMMENTS
algorithm.Once the system is trained, it can recognize the faces of authorized students in real-time. When a student's. face is detected by the camera, the system matches the detected face with ...
Face is one of the most broadly used biometrics for human identity authentication. This paper presents a facial recognition attendance system based on deep learning convolutional neural networks. We utilize transfer learning by using three pre-trained convolutional neural networks and trained them on our data.
Keywords: Local Binary Pattern Histogram(LBPH), Face Detection, Face Recognition, Haarcascade Classifier, Python, Student Attendance. Suggested Citation: Suggested Citation SAI, E CHARAN and HUSSAIN, SHAIK ALTHAF and KHAJA, SYED and SHYAM, AMARA, Student Attendance Monitoring System Using Face Recognition (May 22, 2021).
These methods include RFID-based system, IRIS recognition-based system, and fingerprints-based systems. These systems have their own drawbacks as they are based on queue which is time-consuming. IRIS and fingerprints-based system are considered as privacy threats. In our paper, we have used face recognition approach for our attendance system.
attendance management systems. The underlying technology of face recognition, however, is the same, and can be adapted to suit the proposed system. III. SYSTEM ARCHITECTURE The proposed system uses face recognition to automatically handle attendance of students. Taking the actual face recogni-tion algorithm as a black box, the system decides ...
Survey 1. Face characteristics, or face implementation, are used in this study as biometrics, according to [ 4, 5 ]. This system proposed a technique based on OpenCV that combines a camera that takes a picture of the input, a face recognition algorithm, encoding and identifying the faces, and recording the attendance.
Study of Implementing Automated Attendance System Using Face Recognition Technique. Three primary procedures have been used to put into action the suggested system. Face detection and extraction is the initial procedure. By posing in front of the camera, the user's presence can be detected and used as data.
The system crops and stores the images in a database with corresponding labels and extracts features using algorithms such as LBPH, Haarcascade, Eigen values, support vector machines and Fisher face algorithm. The Face Recognition-based attendance system could help automate attendance records with high accuracy and reduce the burden of manual ...
There are many benefits to attendance management system using face recognition such as its convenience comfort day today life. Attendance management system using face recognition is easy to use and it can be performed without person. Attendance Management System Using Face Recognition gives fast and accurate report of Attendance.
In a classroom, keeping track of attendance is a difficult and time-consuming task. Here comes the importance of automated techniques for attendance marking. Facial recognition is necessary for automated attendance tracking methods. This suggested study uses face detection algorithms to provide an attendance marking system. The primary goals of this endeavor are to accurately record attendance ...
In this paper, a novel CNN architecture for face recognition system is proposed including the process of collecting face data of students. Experimentally it is shown that the proposed CNN architecture provides 99% accuracy. Further, the proposed CNN framework is used to develop a "Smart Attendance Management System (SAMS)", which is a web ...
Abstract: Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. Recognition of the human face is an active issue for authentication purposes specifically in the context of attendance of students. Attendance system using face recognition is a procedure of recognizing students by using face biostatistics based on the high ...
2.2 Face Recognition Based Attendance Marking System The second research journals "Face Recognition Based Attendance Marking System" (SenthamilSelvi, Chitrakala, Antony Jenitha, 2014) is based on the identification of face recognition to solve the previous attendance system‟s issues.
An automatic attendance management system using face detection and recognition are proposed in this paper. The proposed system aims to take the images of the students from classroom videos, to detect and identify their faces from images and upon successful recognition to mark their attendance automatically.
INTRODUCTION: The Attendance System using Face - Recognition is a replacement way method for the traditional way of marking attendance. The proposed system is python, tkinter based system supported with MySQL database. System of a particular institute. This system is proposed to be based on biometrics .i.e. Face Authentication.
Attendance in an educational institution for students is the most challenging part of the virtual platform. It wastes a lot of effort and time when it is done manually. There was also a lot of fake attendance due to lack of a good attendance system. The face is the most important thing which identifies a human. Therefore, in this paper, we work on an attendance system using a face detection ...
Face Recognition-based Attendance Management System Section A-Research paper 720 Eur. Chem. Bull. 2023,12(Special Issue 9), 720-727 ISSN 2063-5346 FACE RECOGNITION-BASED ATTENDANCE MANAGEMENT SYSTEM Ms. Vinaya Kulkarni1 Article History: Received: 10.05.2023 Revised: 29.05.2023 Accepted: 09.06.2023 Abstract
The system utilizes computer vision techniques implemented using Open CV and face recognition libraries for face detection and recognition. Attendance data is stored in a Mongo DB database for easy retrieval and analysis. The system offers a reliable, efficient, and secure solution for attendance management. DOI: /10.61463/ijset.vol.12.issue2.133
Providing accurate attendance marking system in real-time is challenging. It is tough to mark the attendance of a student in the large classroom when there are many students attending the class. Many attendance management systems have been implemented in the recent research. However, the attendance management system based on facial recognition still has issues. Thus many research have been ...