Overview
This project was an early exploration into computer vision and real-time inference. Using Python and OpenCV, I built a live face detection and recognition system that processes webcam input frame-by-frame and overlays detection results directly onto the video stream.
Technical Highlights
• Implemented real-time face detection using OpenCV’s pre-trained models.
• Processed live video streams and performed frame-by-frame inference.
• Built a simple recognition pipeline for identifying known faces.
• Managed real-time performance constraints and visualization overlays.
What I Learned
This project introduced me to core computer vision workflows: image preprocessing, detection models, inference loops, and real-time system design. It also strengthened my understanding of how machine learning models are integrated into interactive applications.
While simple compared to production CV systems, this project laid foundational intuition for working with perception pipelines and ML-driven applications.