International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Facial Recognition based Smart Attendance System Mihir Ghanekar1, Archita Sehgal2, Shrishail Gouragond3, Maitray Wani4, Prof. Pramila M. Chawan5 1,2,3,4B. Tech Student, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India 5Associate Professor, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, Indi
---------------------------------------------------------------------***--------------------------------------------------------------------This research endeavours to harness the potential of face Abstract - In today's digital era, face recognition recognition technology to develop a smart attendance system tailored for educational environments. The proposed system aims to detect student faces from live classroom video streams, thereby streamlining the attendance tracking process and minimizing the likelihood of errors associated with manual methods. By leveraging advancements in face recognition technology and machine learning algorithms, this innovative approach seeks to enhance accuracy, efficiency, and overall classroom management.
technology plays a crucial role in security, authentication, and identification, despite its lower accuracy compared to other biometrics. It presents a promising solution for attendance tracking in educational institutions and workplaces, addressing the inefficiencies of manual processes prone to proxy attendance issues. This research aims to develop a class attendance system leveraging face recognition technology. The proposed system involves database creation, face detection, recognition, and attendance updating using RetinaFace-10GF and ResNet50 models. Through real-time monitoring, attendance records are automatically compiled and accessible to faculty, offering a streamlined and automated solution that enhances accuracy and addresses the shortcomings of traditional attendance tracking methods. By integrating advanced technology with the urgent need for efficient attendance management, this research seeks to contribute to the optimization of attendance processes in various organizational settings.
With the increasing popularity and accessibility of face recognition technology, this paper proposes a comprehensive solution to address the evolving needs of attendance management in educational institutions. By integrating cutting-edge technology with pedagogical practices, the aim is to create a seamless and user-friendly attendance tracking system that optimizes class time and promotes academic accountability. Through this research, we seek to contribute to the ongoing discourse on leveraging technology to enhance teaching and learning experiences in the digital age.
Key Words: Machine Learning, Computer Vision, Face detection, Face recognition, Feature matching, Automated attendance system.
2. LITERATURE REVIEW
1.INTRODUCTION
The evolution of face recognition systems is evident in recent research endeavors, aiming to overcome the limitations of traditional methods that require controlled conditions. Geng underscores the necessity for face recognition under uncontrolled conditions, emphasizing the need for systems capable of operating in real-time without stringent environmental constraints. While such systems offer promise, drawbacks like single-person image input hinder their applicability in scenarios requiring rapid, multi-person detection, such as attendance systems.
In today's educational landscape, the process of manually marking attendance remains a tedious and timeconsuming task for faculty members. This traditional approach not only consumes valuable class time but also introduces the possibility of errors such as proxy attendance. In response to these challenges, educational institutions have explored alternative attendance recording techniques, including Radio Frequency Identification (RFID), iris recognition, and fingerprint scanning. However, these methods often suffer from inefficiencies related to queuing and intrusive procedures.
Winarno introduces an anti-cheating presence system using 3WPCA-Dual Vision Face Recognition, showcasing its effectiveness with a 98% recognition rate. Leveraging stereo vision cameras and the 3WPCA method, this system enhances cheating detection in facial recognition applications, promising greater integrity in attendance monitoring and other security-sensitive contexts.
Face recognition technology emerges as a promising solution to address the shortcomings of traditional attendance tracking methods. Unlike other biometric methods, such as iris or fingerprint recognition, face recognition offers a non-invasive and easily accessible means of identification. Moreover, it is less affected by variations in facial expressions and can efficiently perform both verification and identification tasks.
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