VISAGE ANALYSE APPEARANCE
Authors/Creators
Description
Traditional student attendance methods, such as roll calls and sign-in sheets, are time-consuming, error-prone, and vulnerable to proxy attendance. To address these issues, this study proposes an Automated Attendance Management System (AAMS) that integrates CCTV cameras, facial recognition, and GPS verification. The system captures students' facial images in real-time using classroom CCTV cameras without manual intervention, ensuring seamless and continuous attendance monitoring. Face detection is performed using a Haar Cascaded Classifier, and recognition is achieved through the FaceNet model, which compares captured embedding with a pre-registered student database. Additionally, GPS-based location verification confirms that students are within the classroom’s geofenced area, preventing fraudulent attendance. This dual-verification system offers a secure, accurate, and efficient alternative to traditional attendance methods.
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VISAGE ANALYSE APPEARANCE.pdf
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Additional details
Dates
- Submitted
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2025-07-03Visage Analyze Appearance is an advanced facial analysis project designed to identify individuals and analyze key appearance attributes such as age, gender, and facial features using deep learning techniques. The system employs MTCNN for efficient face detection and for generating unique facial embedding for recognition. By leveraging real-time image processing and computer vision, the project aims to provide accurate, automated, and intelligent analysis of human faces, making it suitable for applications in surveillance, identity verification, and smart human-computer interaction systems.
References
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