AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study

Pattnaik, Priyanka; Mohanty, Kalyan Kumar

doi:10.1109/iceca49313.2020.9297643

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…However, OpenCV [25] suffers from the problems of missed detections, false detections, and in effect poor recognition overall. On the other hand, Dlib is a cutting-edge toolbox which includes various different AI tools and which allows the creation of complex software programs for solving real-world problems [26]. Dlib can deal with bad and inconsistent lighting and various facial positions (such as tilted or rotated).…”

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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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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Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics

Iype,

Sebastian

2024

Communications in Computer and Information Science

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Ethical principles for artificial intelligence in education

et al. 2022

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The advancement of artificial intelligence in education (AIED) has the potential to transform the educational landscape and influence the role of all involved stakeholders. In recent years, the applications of AIED have been gradually adopted to progress our understanding of students’ learning and enhance learning performance and experience. However, the adoption of AIED has led to increasing ethical risks and concerns regarding several aspects such as personal data and learner autonomy. Despite the recent announcement of guidelines for ethical and trustworthy AIED, the debate revolves around the key principles underpinning ethical AIED. This paper aims to explore whether there is a global consensus on ethical AIED by mapping and analyzing international organizations’ current policies and guidelines. In this paper, we first introduce the opportunities offered by AI in education and potential ethical issues. Then, thematic analysis was conducted to conceptualize and establish a set of ethical principles by examining and synthesizing relevant ethical policies and guidelines for AIED. We discuss each principle and associated implications for relevant educational stakeholders, including students, teachers, technology developers, policymakers, and institutional decision-makers. The proposed set of ethical principles is expected to serve as a framework to inform and guide educational stakeholders in the development and deployment of ethical and trustworthy AIED as well as catalyze future development of related impact studies in the field.

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AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study

Cited by 10 publications

References 18 publications

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

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

Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics

Ethical principles for artificial intelligence in education

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