https://github.com/prcharan592/seamless-attendance-automation-using-facial-recognition-
The system uses facial recognition to automatically identify registered users in real-time via webcam, leveraging OpenCV for face detection and KNN for identification. Attendance data is stored in CSV files and managed through a Flask web application for seamless user interaction.
https://github.com/prcharan592/seamless-attendance-automation-using-facial-recognition-
knn-algorithm machine-learning opencv python
Last synced: about 2 months ago
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The system uses facial recognition to automatically identify registered users in real-time via webcam, leveraging OpenCV for face detection and KNN for identification. Attendance data is stored in CSV files and managed through a Flask web application for seamless user interaction.
- Host: GitHub
- URL: https://github.com/prcharan592/seamless-attendance-automation-using-facial-recognition-
- Owner: prcharan592
- Created: 2025-01-09T08:36:41.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-17T05:56:37.000Z (4 months ago)
- Last Synced: 2025-01-17T06:31:02.306Z (4 months ago)
- Topics: knn-algorithm, machine-learning, opencv, python
- Language: Python
- Homepage:
- Size: 294 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
# Seamless Attendance Automation Using Facial Recognition
This system revolutionizes the traditional attendance process by leveraging facial recognition technology for seamless and automated attendance tracking. Designed to identify registered users in real-time through a webcam, the system uses OpenCV for efficient face detection and the K-Nearest Neighbors (KNN) algorithm for accurate identification.
# Key Features:
• Real-Time Identification: The system captures live video feed and identifies users in real-time, ensuring swift and accurate attendance logging.
• Facial Recognition: Advanced face detection algorithms ensure reliability and precision, even in dynamic environments.
• Data Management: Attendance records are systematically stored in CSV files for easy access and analysis.
• User-Friendly Interface: Built using Flask, the web application provides an intuitive interface for managing user data, attendance reports, and system settings.
• Scalability and Security: The architecture ensures scalability to accommodate a large user base while maintaining data integrity and security.
This project demonstrates the practical application of AI and machine learning techniques in everyday processes, offering a robust, contactless, and time-efficient attendance solution.