🔹 Description

The Facial Recognition Attendance System is an intelligent application designed to automate attendance tracking using computer vision and deep learning techniques. Instead of manual entry or biometric devices, the system identifies individuals through facial detection, ensuring accuracy and efficiency.

Using YOLOv8 for real-time face detection, the system captures video input and identifies faces with high precision. OpenCV handles image processing and live video streaming, while deep learning models trained using PyTorch ensure reliable recognition across different lighting and facial variations.

Once a face is detected and recognized, attendance data is automatically stored in a MySQL database with timestamps. This eliminates proxy attendance, reduces manual errors, and saves administrative time.

The system can be applied in schools, colleges, offices, and secure facilities. This project highlights expertise in AI-based identification systems, database integration, and real-world automation solutions.

🔹 Key Features

• Real-time facial detection and recognition
• Automated attendance marking
• YOLOv8-based face detection
• Secure attendance storage in MySQL
• Live camera feed processing with OpenCV
• Reduced manual effort and human error

🔹 Technologies Used

• Programming Language: Python
• Computer Vision: OpenCV
• Deep Learning Framework: PyTorch
• Object Detection Model: YOLOv8
• Database: MySQL