Attendance Management System

Kodali, Ravi Kishore; Hemadri, Raghu Vamshi

doi:10.1109/iccci50826.2021.9402659

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…With improved technology resources and computation are no more limitations. Accuracy can be improved by using proper datasets for training, good environmental conditions for testing and also it can be improved by improving the algorithms used for face recognition [8]. Many face recognition techniques were introduced out of which we use, open-source computer vision library, NumPy, Python (Tkinter) so as to make our application runs faster.…”

Section: Introductionmentioning

confidence: 99%

Attendance management system using face recognition

Jadhav,

Kamble,

Rathod

et al. 2024

IJ-AI

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<p>Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.</p>

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Section: Introductionmentioning

confidence: 99%

Attendance management system using face recognition

Jadhav,

Kamble,

Rathod

et al. 2024

IJ-AI

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“…Deep learning involves the use of artificial neural network structures with many functional layers, as opposed to the "swallower" topologies utilised in more traditional neural network methodologies. [1] In this paper they found the solution for an automatic attendance system by using facial recognition. This paper mainly focuses on the methods and techniques required to mark the attendance.…”

Section: Introductionmentioning

confidence: 99%

“…This paper mainly focuses on the methods and techniques required to mark the attendance. [1] This system uses a deeply supervised neural network for recognizing the faces. They have developed a web application interface which is easy for users to understand.…”

Section: Introductionmentioning

confidence: 99%

Attendance and Performance Monitoring Using ML

Shahu¹,

N²,

Reddy³

et al. 2022

IJRASET

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Attendance Management is very important for every organizations. Marking and maintaining attendance is a very time consuming task. It takes lots of time to mark attendance manually. It is difficult to analyse attendance of students how frequently one is skipping classes. There will be some problems regarding proxy attendance of some students. The possible solution to this problem is to use an automatic attendance system which uses face recognition techniques. This system will mark attendance electronically and recorded attendance will be stored in a database. The preparation of a question paper for the Internal Assessment Exam can be automatically generated with the help of teachers according to RBT levels. The Internal Assessment is very important for students. This system will give individual student reports based on the RBT levels of students' performance in the internal assessment exam and can be analysed, exactly in which type of RBT level the student is lagging behind. Keywords: Attenndance Monitoring; deep learning; convolutional neural networks (CNN), Machine Learning, paper setter, Internal Assesment Analysis, RBT(Revised Blooms Taxonomy) can be used;

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