Abstract
Face Recognition (FR) is a biometric technique that involves determining whether the image of a given person’s face matches any of the face images stored in a database. As a key attributes of biometric ratification, FR is widely utilized in different types of administration systems for video surveillance, computer human interface, indoor access systems, & network security. The proposed scheme is designed for student detection and recognition for tracking student attendance. As a result, Smart Attendance with Real Time Face Recognition (SARTFR) is a practical solution for day to day employee management activities. SARTFR is proposed based on Viola Jones Algorithm and LBP methods. SARTFR has got better results in terms of detection, recognition and tracking from the results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal A, Bansal A (2013) Online attendance management system using RFID with object counter. Int J Inf Comput Technol 3(3):131–138
Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: European conference on computer vision. Springer, pp 469–481
Barnouti NH, Al-Dabbagh SSM, Matti WE, Naser MAS (2016) Face detection and recognition using viola-jones with PCA-LDA and square Euclidean distance. Int J Adv Comput Sci Appl (IJACSA) 7(5):371–377
Bhise A, Khichi R, Korde A, Lokare P (2015) Attendance system using NFC technology with embedded camera on mobile device. Int J Adv Res Comput Commun Eng 4(2):350–353
Chen JIZ, Chang JT (2020) Applying a 6-axis mechanical arm combine with computer vision to the research of object recognition in plane inspection. J Artif Intell 2(02):77–99
Jacob IJ, Darney PE (2021) Design of deep learning algorithm for IoT application by image based recognition. J ISMAC 3(03):276–290
Kambi Beli IL, Guo C (2017) Enhancing face identification using local binary patterns and k-nearest neighbors. J Imaging 3(3):37
Krishnan MG, Balaji SB (2015) Implementation of automated attendance system using face recognition. Int J Sci Eng Res 6(3)
Nisha MD (2017) Improving the recognition of faces using LBP and SVM optimized by PSO technique. Int J Eng Dev Res 5(4):297–303
Okokpujie KO, Noma-Osaghae E, Okesola OJ, John SN, Robert O (2017) Design and implementation of a student attendance system using iris biometric recognition. In: 2017 International conference on computational science and computational intelligence (CSCI). IEEE, pp 563–567
Perumal RS, Mouli PC (2018) A comparative analysis of local pattern descriptors for face recognition. In: Knowledge computing and its applications. Springer, pp 129–154
Rahim MA, Azam MS, Hossain N, Islam MR (2013) Face recognition using local binary patterns (LBP). Glob J Comput Sci Technol
Ramalingam SP, Mouli C (2020) Face analysis using row and correlation based local directional pattern. ELCVIA Electron Lett Comput Vision Image Anal 19(3):55–70
Saibaba G, Sanivarapu PV (2018) Developing an userfriendly online shopping web-site. Indonesian J Electr Eng Comput Sci 12(3):1126–1131
Sanivarapu PV (2021) Multi-face recognition using cnn for attendance system. In: Machine learning for predictive analysis. Springer, pp 313–320
Sharma R, Sungheetha A (2021) An efficient dimension reduction based fusion of CNN and SVM model for detection of abnormal incident in video surveillance. J Soft Comput Paradigm (JSCP) 3(02):55–69
Singh A, Vaidya SP (2019) Automated parking management system for identifying vehicle number plate. Indonesian J Electr Eng Comput Sci 13(1):77–84
Srinivasa Perumal R, Priya G, Mouli PC (2019) Face spoofing detection using dimensionality reduced local directional pattern and deep belief networks. In: International conference on deep learning, artificial intelligence and robotics. Springer, pp 330–338
Vijayakumar T (2021) Synthesis of palm print in feature fusion techniques for multimodal biometric recognition system online signature. J Innov Image Process (JIIP) 3(02):131–143
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol 1. Ieee, pp I–I
Author information
Authors and Affiliations
Corresponding author
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
Aparna, G., Prasanth Vaidya, S. (2022). Smart Attendance with Real Time Face Recognition. In: Suma, V., Baig, Z., Kolandapalayam Shanmugam, S., Lorenz, P. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-19-1012-8_59
Download citation
DOI: https://doi.org/10.1007/978-981-19-1012-8_59
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1011-1
Online ISBN: 978-981-19-1012-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)