🧔 Real-Time Face Detector

This project marks the transition from classical, rule-based computer vision to using pre-trained machine learning models. It's a real-time application that utilizes a webcam to detect human faces in a live video stream and draws bounding boxes around them.

Concepts and Skills Demonstrated

The key skills and concepts demonstrated are:

• Real-Time Video Processing: Capturing a video stream from a webcam and processing it frame-by-frame with OpenCV.
• Machine Learning Model Implementation: Loading and using a pre-trained object detection model.
• Haar Cascades: A foundational understanding of the theory behind the Haar Cascade algorithm, including its feature-based, cascaded architecture for rapid object detection.
• Parameter Tuning: Practical application of detectMultiScale and an understanding of its key tuning parameters (scaleFactor, minNeighbors) to balance speed and accuracy.