This GitHub repository houses a machine learning project that employs state-of-the-art technologies to automate attendance management. The project utilizes OpenCV for image processing, the K-Nearest Neighbors Classifier for facial recognition, Streamlit for a user-friendly interface, and Heroku for seamless deployment. With this system, you can enjoy the benefits of automatic attendance tracking through facial recognition, all while conveniently checking your attendance records from your mobile device.
- OpenCV: Harness the power of Open Source Computer Vision Library for precise face detection and image processing.
- K-Nearest Neighbors Classifier: Employ a robust machine learning algorithm to recognize faces with high accuracy.
- Streamlit: Experience a user-friendly and interactive web application for a smooth user interface.
- Heroku: Access the project anytime, anywhere, thanks to its deployment on the Heroku cloud platform.
- Real-time facial recognition for automated attendance marking.
- Secure and easy-to-use web interface for managing attendance records.
- Mobile accessibility for checking attendance on the go.