Automated attendance management systems: systematic literature review

Ali, Nabeel Salih; Alhilali, Ahmed Hazim; Rjeib, Hasanein D.; Alsharqi, Haider; Sadawi, Basheer Al

doi:10.1504/ijtel.2022.120559

Cited by 11 publications

(3 citation statements)

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“…The experiment outcomes demonstrate that DenseNet121 is the ideal model for handling practical matters. The study Ali et al (2022) offered a comprehensive analysis of the attendance management tools with great promise for managing, documenting, and monitoring user presence across several domains. An extensive literature review framework for classifying papers is also presented in this research.…”

Section: Introductionmentioning

confidence: 99%

Next-generation attendance tracking: Automated face detection for lecture attendance

Gupta,

Jain,

Bhat

2023

Multidiscip. Sci. J.

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Face detection technology has garnered much interest due to its potential uses in industries like face identification and video surveillance systems. In addition to being a component of the automatic face recognition framework, real-time face detection is now becoming its study area. There are numerous methods to handle the problem of face detection. Traditional methods of manual attendance tracking in lecture halls are time-consuming, prone to errors, and lack efficiency. Next-generation attendance tracking systems are incorporating automated face detection technology to address these challenges. This paper explores the implementation of automated face detection for lecture attendance, leveraging computer vision and facial recognition algorithms. Initially, students’ data were gathered and preprocessed using a Median filter (MF). Then the preprocessed data features were extracted using a histogram of gradients (HOG). And finally, extracted features were classified using a Backpropagation Bayesian neural network (BBNN). To prove the efficiency of the suggested method, that is compared and contrasted with specific conventional methods. Experimental outcomes show that the recommended method performs superior when contrasted to traditional methods.

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Section: Introductionmentioning

confidence: 99%

Next-generation attendance tracking: Automated face detection for lecture attendance

Gupta,

Jain,

Bhat

2023

Multidiscip. Sci. J.

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“…The SSMS app is an innovative solution that has the potential to significantly improve the quality of education in Sindh by promoting accountability, transparency, and data-driven decision-making. However it is reported as a critical issue [4,5] and there are several studies are presented by researchers. The implementation of biometric attendance systems in HEIs has a positive impact on attendance monitoring, accountability, and transparency as suggested by authors in [6].…”

Section: Introductionmentioning

confidence: 99%

An Improved Framework for Sindh School Monitoring System Android App

Halepoto,

Gul,

Memon

et al. 2023

VFAST trans. softw. eng.

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Sindh government has presented a system for observing schools called the Sindh School Monitoring System (SSMS) Framework. One of the part of the system is SSMS app, which is based on Android. SSMS app is widely used in the Sindh province in order to monitor the school with major focus on attendance. The SSMS app has increased the system performance in terms of attendance, however several flaw are present in its current framework. This paper identifies the key issues in the current framework such as identification and verification of Monitoring assistant (MA), school search options and SMC, teacher performance evaluation, reporting, curriculum, student performance evaluation and census, school building details, SNE, school amenities, GR register, NADRA verification, rights of MAs and online reporting options in the app. The changes are proposed in the existing framework, for the said key issues, which could improve the overall system performance. In order to validate the key findings and proposed changes in the existing framework a questionnaire has been prepared and evaluated from the SSMS app users. All the app users validated the proposed changes in the framework.

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“…However, of late, information technology [2][3][4][5] has been widely used to provide convenience, speed up the task, and also streamline it. For the purposes of AAS, various technologies, such as facial recognition, iris detection, and radio frequency identification (RFID), have been used [6]. RFID systems utilize sensors in order to read data.…”

Section: Introductionmentioning

confidence: 99%

The Application of Adaptive Tolerance and Serialized Facial Feature Extraction to Automatic Attendance Systems

Lin

Huang

2022

Electronics

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The aim of this study was to develop a real-time automatic attendance system (AAS) based on Internet of Things (IoT) technology and facial recognition. A Raspberry Pi camera built into a Raspberry Pi 3B is used to transfer facial images to a cloud server. Face detection and recognition libraries are implemented on this cloud server, which thus can handle all the processes involved with the automatic recording of student attendance. In addition, this study proposes the application of data serialization processing and adaptive tolerance vis-à-vis Euclidean distance. The facial features encountered are processed using data serialization before they are saved in the SQLite database; such serialized data can easily be written and then read back from the database. When examining the differences between the facial features already stored in the SQLite databases and any new facial features, the proposed adaptive tolerance system can improve the performance of the facial recognition method applying Euclidean distance. The results of this study show that the proposed AAS can recognize multiple faces and so record attendance automatically. The AAS proposed in this study can assist in the detection of students who attempt to skip classes without the knowledge of their teachers. The problem of students being unintentionally marked present, though absent, and the problem of proxies is also resolved.

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Automated attendance management systems: systematic literature review

Cited by 11 publications

References 0 publications

Next-generation attendance tracking: Automated face detection for lecture attendance

Next-generation attendance tracking: Automated face detection for lecture attendance

An Improved Framework for Sindh School Monitoring System Android App

The Application of Adaptive Tolerance and Serialized Facial Feature Extraction to Automatic Attendance Systems

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