Automated Student Attendance System Using Face Recognition

Akay, E. Omer; Canbek, Kansu Oguz; Oniz, Yesim

doi:10.1109/ismsit50672.2020.9255052

Cited by 15 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A general approach has been proposed for attendance administration using a WI-FI system. [34] Facial recognition and mask detection (which are not misused) were used, and various algorithms were applied to perform the automatic attendance registration process, saving these records in a database, where it is concluded that it provides greater accuracy in conditions of higher illumination. [35] An intelligent automatic attendance monitoring system was developed with the biometric facial recognition system.…”

Section: Review Resultsmentioning

confidence: 99%

“…Related studies Prevent impersonation and false assistance [21], [22], [27], [28], [30], [31], [32], [34], [35], [38], [39], [40], [41] Saving time and work for teachers and students [21], [23], [33], [34], [37], [38], [41], [43] Increasing the security and reliability of data [21], [24], [25], [26], [27], [28], [29], [35], [36], [37], [38], [42] Increases productivity [23] Improving work culture, accountability, and transparency [23], [40], [41], [43] Generates a clear audit trail for attendance records [23] Biometric traits do not change or are difficult to alter over time.…”

Section: Benefitsmentioning

confidence: 99%

“…[25], [37] Improves verification accuracy in constrained scenarios. [25] Profitable [28], [29], [30] easy to use and reliable [28], [30] Avoiding the risk of human error [31], [33] Ensures minimal human intervention and manipulation of records [33] Avoid paper waste [33] Increases accuracy [34], [35] Check compliance with regulations (such as the use of masks). [34] Improved usability [36] Integrate with other technologies to create better classroom environments and improve student learning and motivation.…”

Section: Benefitsmentioning

confidence: 99%

“…Several articles point out that one of the benefits of using biometric technology is the prevention of identity theft and attendance forgery [21], [22], [27], [28], [30], [31], [32], [34], [35], [38], [39], [40], [41], [27], [28], [30], [31], [32], [34], [35], [38], [39], [40], [41] which contributes to the integrity of records [21], [24], [25], [26], [27], [28], [29], [35], [36], [37], [38], [42]. Likewise, biometric technology saves time and work for teachers and students, as these technologies simplify attendance taking and reduce the administrative burden for both teachers and students [21], [23], [33], [34], [37], [38], [41], [43], thus fostering a culture of accountability and transparency in the educational environment [23],…”

Section: Benefitsmentioning

confidence: 99%

See 3 more Smart Citations

Systematic Literature Review: Biometric Technology Applied to Educational Institutions

Necochea-Chamorro,

Sotelo Asalde,

Loli Nuñez

et al. 2024

TEM Journal

View full text Add to dashboard Cite

Biometric technology, founded on recognizing distinctive individual features, enhances identification and automation, yielding personal and institutional benefits. This literature review employs the Kitchenham and Charters methodology to analyze recent perspectives on biometric technology in educational time and attendance. The analysis identifies benefits, such as preventing impersonation and enhancing data security, but emphasizes concerns about biometric alterations and hardware/software failures hindering optimal outcomes. A noteworthy trend is adopting a bimodal behavioral approach in biometric technologies in education. The study recommends research avenues focusing on privacy safeguards, biometric system robustness, and technological resilience. Beyond mere identification, a shift towards quantitative and descriptive methods is proposed, guiding future research. The results underscore the importance of a multimodal approach and stringent security measures to address biometric system vulnerabilities. Future exploration should consider integrating emerging technologies to enhance system resilience and data protection, providing a comprehensive guide for refining and expanding biometric technology applications in education.

show abstract

Section: Review Resultsmentioning

confidence: 99%

Section: Benefitsmentioning

confidence: 99%

Section: Benefitsmentioning

confidence: 99%

Section: Benefitsmentioning

confidence: 99%

See 2 more Smart Citations

Systematic Literature Review: Biometric Technology Applied to Educational Institutions

Necochea-Chamorro,

Sotelo Asalde,

Loli Nuñez

et al. 2024

TEM Journal

View full text Add to dashboard Cite

show abstract

“…E. Omer Akay et al [6] exhibits two di↵erent face recognition algorithms, in particular Histogram of Oriented Gradients and Haar-Cascade algorithms, that are applied and their performances are measured. Deep learning in view of convolutional neural networks (CNNs) is utilized for the identification of the students in the classroom.…”

Section: Literature Surveymentioning

confidence: 99%

Supervisory framework for threat detection withmultilayer processing in CNN

Mule¹,

Pandit²,

Karmakar³

et al. 2022

ITM Web Conf.

View full text Add to dashboard Cite

Face recognition has received huge acknowledgement due to its various uses in Internet communication, security, access control, surveillance, PC entertainment and law enforcement. Conventional methods of recognition based on the ownerships of identity-cards or full knowledge such as a security number or password are not totally solid. Physical ID cards can be lost or forged, passwords can be hacked or forgotten but a face is undoubtedly connected to its owner. It cannot be stolen, borrowed or easily forged. Our current system has a lot of weaknesses wherever it is simply taken and merged. The focus of this paper is to help users for development of the security by utilizing face identification and recognition. The proposed framework principally comprises of subsystems specifically picture capture, face identification and detection, email alerts and metal detection. Furthermore, the improvement in Computer Vision through Deep Learning algorithms has been an impressive achievement, especially with the Convolutional Neural Network algorithm. A convolutional neural network is a feed-forward neural network that is by and large used to break down visual pictures by using grid-like topology. It is also called as ConvNet. The objects in a picture are distinguished and arranged using convolutional neural network. CNN detects various simple complex patterns in images and data in its different layers of Convolution Layer, Max Polling Layer and Fully Connected Layer .This field intends to allow and configure machines to see the world as people do, and utilize the information for doing tasks (such as Image Analysis, Image Recognition and Classification, etc).

show abstract