Smartphone application for tracking students' class attendance

Čisar, Sanja Maravić; Pintér, Róbert; Vojnic, Viktor; Tumbas, Vanja; Čisar, Petar

doi:10.1109/sisy.2016.7601502

Cited by 12 publications

(5 citation statements)

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“…Many students have some sort of smart device and these can be used to create an app and allow students to sign in to participate. The work of [15] using its established Muffin software has created one possible solution. Muffin is a mobile application that is available to students on their personal mobile devices, a Bluetooth modulated Arduino Uno board, and a computer app.…”

Section: Related Literaturementioning

confidence: 99%

“…In recent trends in modern industries, automated attendance system has been anticipated by using several identification strategies like biometric recognitions such as palm vein [3], [4], fingerprint [5], [6], and face recognition [7], [9] to recognized and mark the students as present. The other proposed systems for tracking attendance used RFID [10], [11], barcode [12], QR code [13], mobile devices that uses Near Field Communication (NFC) [14], and some systems developed in a portable device like smartphone [15].…”

Section: Introductionmentioning

confidence: 99%

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A YOLOv3 Inference Approach for Student Attendance Face Recognition System

Alon¹

2020

IJETER

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Checking attendance in a classroom is a factor contributing to the final performance of the students in the course. For both students and professors, attendance checking by name is very time-consuming and, in particular, the latter is very susceptible to simple attendance fraud. The study used a Face Recognition based attendance method using the YOLOv3 approach as an alternative. The system, based on face-detection and face-recognition algorithms, automatically recognizes, and marks attendance by recognizing the student. The experimental result shows that by using the trained model with a training accuracy of 98.01%, the proposed attendance system achieved 94% face recognition efficiency.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A YOLOv3 Inference Approach for Student Attendance Face Recognition System

Alon¹

2020

IJETER

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“…Recently, some instructors use smartphones to capture class attendance. Pinter et al [25] design an application for smartphones based on the bluetooth technique. When taking attendance, students turn on the bluetooth of their smartphones and choose a class from a class list for registering.…”

Section: Related Workmentioning

confidence: 99%

Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Pei

Zhang

et al. 2019

Electronics

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Class attendance is an important means in the management of university students. Using face recognition is one of the most effective techniques for taking daily class attendance. Recently, many face recognition algorithms via deep learning have achieved promising results with large-scale labeled samples. However, due to the difficulties of collecting samples, face recognition using convolutional neural networks (CNNs) for daily attendance taking remains a challenging problem. Data augmentation can enlarge the samples and has been applied to the small sample learning. In this paper, we address this problem using data augmentation through geometric transformation, image brightness changes, and the application of different filter operations. In addition, we determine the best data augmentation method based on orthogonal experiments. Finally, the performance of our attendance method is demonstrated in a real class. Compared with PCA and LBPH methods with data augmentation and VGG-16 network, the accuracy of our proposed method can achieve 86.3%. Additionally, after a period of collecting more data, the accuracy improves to 98.1%.

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“…[9][10] Proposed a mobile application intended to check the attendance of students using facial recognition techniques. [11] Produced mobile application called Muffin, consisting of the mobile application, Arduino board, and a PC. Muffin was designed to track the attendance of students in classes.…”

Section: Objectives Of the Proposed Student Attendance Systemmentioning

confidence: 99%

Monitoring Student Attendance Using a Smart System at Taif University

Alghamdi¹

2019

IJCSIT

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The university system in the Kingdom of Saudi Arabia is concerned with student attendance for lectures, and it is the responsibility of lecturers to monitor student attendance for each lecture. By the end of the semester, students get an attendance register indicating which lectures the student has attended and it reports the calculated percentage for each student's attendance in each course. Universities have regulated the mechanisms and the acceptable percentages of student absence. The process for a lecturer to manually check student attendance consumes a lot of time and effort, either during the lecture or when in the process of emptying absenteeism and inserting it into the university's electronic system. Therefore, Saudi universities compete to find modern methods of checking student attendance that will avoid the disadvantages of manually taking attendance. For this reason, they have produced electronic attendance systems, for example, using a student's fingerprint, an eye recognition system, or a mobile phone system to read a QR code designed for the same purpose. All of these systems have the disadvantage that they consume a lot of time, as all students have to line up at the fingerprint reader or the eye detector for identification. Therefore, the problem of the consumption of lecture time is still present, even with these modern systems. Therefore, the aim of this research is to propose a smart mobile application that is able to check the attendance of students without having to consume lecture time or require any effort from the lecturer. The system automatically recognizes the attendance of students through their university ID cards. Each lecturer would use his/ her own mobile phone to use the proposed system to check the attendance of students instead of using manual method to register the attendance of students and the students' ID cards that are detected by coming within range of the lecturer reader would represent present students, and missing student ID cards represent absent students

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Smartphone application for tracking students' class attendance

Cited by 12 publications

References 0 publications

A YOLOv3 Inference Approach for Student Attendance Face Recognition System

A YOLOv3 Inference Approach for Student Attendance Face Recognition System

Face Recognition via Deep Learning Using Data Augmentation Based on Orthogonal Experiments

Monitoring Student Attendance Using a Smart System at Taif University

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