Implemented a Face Recognition System using Python and OpenCV to detect, encode, and recognize faces from images or real-time video streams. The project demonstrates data preprocessing, feature extraction, and real-time face matching with accuracy evaluation.
Detect and recognize human faces using OpenCV. Encode and compare facial features. Perform real-time recognition via webcam. Evaluate recognition accuracy and reliability.
Face Detection – Used OpenCV’s Haar Cascade or face_recognition library for identifying faces in frames. Feature Extraction – Encoded facial features into numerical vectors. Model Training – Stored face encodings and labels for known individuals. Recognition & Matching – Compared new faces against stored encodings. Performance Evaluation – Calculated recognition accuracy and response time. Tech Stack Python 3.x OpenCV NumPy face_recognition (dlib-based)
Clone and run the notebook: git clone https://github.com//Face-Recognition-Project.git
cd Face-Recognition-Project
jupyter notebook "Face Recognition Project.ipynb"
Real-time webcam face recognition. Bounding boxes and labels for identified faces. Accuracy metrics and visual performance summary.
The system successfully detects and recognizes multiple faces in real time with high accuracy and minimal latency.
Developed by Gresa Hisa — AI & Cybersecurity Engineer