Face Recognition Technology for Automatic Attendance System
Keywords:
face detection, face recognition, Haar features, histogram of oriented gradient, PIR sensorAbstract
The attendance system is essential in schools and colleges. There are several drawbacks to manual attendance systems, including the fact that they are less dependable and difficult to maintain. This enhances accuracy while requiring less time than previous ways using an attendance system using facial recognition technology. There are several current attendance systems, such as IoT facial detection, PIR, and so on. For facial recognition, hardware devices are also helpful. The problem is to ensure that all sensors function well without damage. The aim is to use the hair cascade algorithm to create a system with the best accuracy of all of the methods and methods. Images may be taken between 50 and 70 cm away. A graphical user interface is meant to let users with one click to collect images, build datasets and train datasets. After recognition of the face, it shows the student's name and roll number. In the attendance sheet, the information is automatically provided together with the date and time.
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References
.He X, Yan S, Hu Y, Niyogi P, Zhang HJ. Face recognition
using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell. 2005;
.Jain S, Jain NK, Mishra S. EHCPRs system as an ontology learning system. In: 2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015. 2015.
.Singh P, Yadava RDS. Quantitative Identification of Volatile Organics by SAW Sensor Transients - Comparative Performance Analysis of Fuzzy Inference and Partial-Least-Square-Regression Methods. In: Advances in Intelligent Systems and Computing. 2014.
.Ather D, Singh R, Katiyar V. An algorithm to design finite automata that accept strings over input symbol a and b having exactly x number of a & y number of b. In: Proceedings of the 2013 International Conference on Information Systems and Computer Networks, ISCON 2013. 2013.
.Asrani D, Jain R. Review of techniques used in data warehouse implementation: An initiative towards designing a frame work for effective data warehousing. In: 2014 International Conference on Advances in Engineering and Technology Research, ICAETR 2014. 2014.
.Jain A, Kumar A, Sharma S. Comparative design and analysis of mesh, torus and ring NoC. In: Procedia Computer Science. 2015.
.Mishra S, Malik S, Jain NK, Jain S. A Realist Framework for Ontologies and the Semantic Web. In: Procedia Computer Science. 2015.
.Sharma M, Garg RB, Dwivedi S. Comparative analysis of NPN algorithm & des Algorithm. In: Proceedings - 2014 3rd International Conference on Reliability, Infocom Technologies and Optimization: Trends and Future Directions, ICRITO 2014. 2015.
.Khan FN, Govil K, Agarwal A. Reliability based routing strategy for performance optimization in distributed mobile computing through clustering. In: Proceedings of 2014 3rd International Conference on Parallel, Distributed and Grid Computing, PDGC 2014. 2015.
.Suhas S. S. Face Recognition Using Principal Component Analysis and Linear Discriminant Analysis on Holistic Approach in Facial Images Database. IOSR J Eng. 2012;
.Haroon M, Husain M. Interest attentive dynamic load balancing in distributed systems. In: 2015 International Conference on Computing for Sustainable Global Development, INDIACom 2015. 2015.
.Xia H, Jia Z, Li X, Ju L, Sha EHM. Trust prediction and trust-based source routing in mobile ad hoc networks. Ad Hoc Networks. 2013;
.Gupta P, Tyagi N. An approach towards big data - A review. In: International Conference on Computing, Communication and Automation, ICCCA 2015. 2015.
.Bala L, K. Vatsa A. Quality based Bottom-up-Detection and Prevention Techniques for DDOS in MANET. Int J Comput Appl. 2012;
.Dutta C, Singhal N. A cross validated clustering technique to prevent road accidents in VANET. In: Proceedings of the 2018 International Conference on System Modeling and Advancement in Research Trends, SMART 2018. 2018.
.Singh P, Tyagi N. Radial Basis Function For Handwritten Devanagari Numeral Recognition. Int J Adv Comput Sci Appl. 2011;
.Gupta H, Kumar S, Yadav D, Verma OP, Sharma TK, Ahn CW, et al. Data analytics and mathematical modeling for simulating the dynamics of COVID-19 epidemic—a case study of India. Electron. 2021;
.Kosov S, Thormählen T, Seidel HP. Rapid stereo-vision enhanced face recognition. In: Proceedings - International Conference on Image Processing, ICIP. 2010.
.Zhao W, Chellappa R, Krishnaswamy A. Discriminant
analysis of principal components for face recognition. In: Proceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998. 1998.
.Kanade T. Picture Processing System by Computer Complex and Recognition of Human Faces. doctoral dissertation, Kyoto University. 1973.
.Levada ALM, Correa DC, Salvadeo DHP, Saito JH, Mascarenhas NDA. Novel approaches for face recognition: Template-matching using dynamic time warping and LSTM neural network supervised classification. In: Proceedings of IWSSIP 2008 - 15th International Conference on Systems, Signals and Image Processing. 2008.
.Jha A, Kumar M. Two wheels differential type odometry for mobile robots. In: Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization. IEEE; 2014. p. 1–5.
.Wagh P, Thakare R, Chaudhari J, Patil S. Attendance system based on face recognition using eigen face and PCA algorithms. In: Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015. 2016.
.Joseph J, Zacharia KP. Automatic Attendance Management System Using Face Recognition. Int Res J Eng Technol. 2017;
.Year Student F, Professor A. Student Attendance Marking Using Face Recognition in Internet of Things. Int J Comput Sci Trends Technol. 2013;