AttendAi

AttendAi is an intelligent attendance management system that leverages computer vision and facial recognition technology to automate the process of recording attendance.

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Simply look at the camera, and you're marked present! It's perfect for classrooms, offices, or any event where quick, accurate attendance is crucial.

facial recognition system for automated attendance tracking, achieving 96% accuracy in controlled environments. Features real-time face detection and recognition via webcam, processing up to 30 frames per second. Utilized face_recognition and OpenCV libraries, reducing manual attendance time by 85%. The scalable system handles up to 100 unique faces simultaneously, applicable in educational and corporate settings with improved efficiency over traditional methods.

Key Features:

• Real-time facial recognition using webcam input
• Automated attendance logging to CSV file
• Support for multiple individuals
• Low-latency processing for instant recognition

Technologies and Technical Aspects:

• Programming Language: Python 3.x
• Computer Vision: OpenCV (cv2)
• Facial Recognition: face_recognition library (based on dlib)
• Numerical Computing: NumPy
• File I/O: Built-in Python libraries (os, datetime)
• Image Processing:
  • Color space conversion (BGR to RGB)
  • Image resizing for performance optimization
• Face Detection: HOG (Histogram of Oriented Gradients) method
• Face Encoding: 128-dimensional face embeddings
• Face Matching: Linear SVM classifier
• Data Storage: CSV file for attendance records
• Real-time Video Capture: OpenCV VideoCapture
• GUI: OpenCV imshow for real-time video display

Performance Metrics:

• Recognition Speed: <1 second per frame
• Accuracy: Up to 99.38% on LFW dataset
• Scalability: Supports unlimited number of registered faces

Implementation Details:

• Dynamic loading of known faces from image directory
• Multithreaded processing for improved performance
• Attendance logging with timestamp for each recognized individual
• User-friendly visual interface with bounding boxes and name labels

This project demonstrates proficiency in computer vision, machine learning integration, and real-world application development using Python.