https://github.com/nour-zayed/face-recogntion
This project implements a real-time face recognition system using Computer Vision and Deep Learning. The system is capable of detecting and recognizing faces in real-time using a webcam, as well as from images and video files. The project leverages OpenCV, dlib, and the face_recognition library to accurately detect, encode, and identify faces.
https://github.com/nour-zayed/face-recogntion
database dlib face-recognition numpy opencv python
Last synced: 2 months ago
JSON representation
This project implements a real-time face recognition system using Computer Vision and Deep Learning. The system is capable of detecting and recognizing faces in real-time using a webcam, as well as from images and video files. The project leverages OpenCV, dlib, and the face_recognition library to accurately detect, encode, and identify faces.
- Host: GitHub
- URL: https://github.com/nour-zayed/face-recogntion
- Owner: Nour-Zayed
- Created: 2025-02-27T21:53:28.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-27T22:14:16.000Z (over 1 year ago)
- Last Synced: 2025-02-28T06:48:11.716Z (over 1 year ago)
- Topics: database, dlib, face-recognition, numpy, opencv, python
- Language: Python
- Homepage:
- Size: 891 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Face-Recogntion
This project implements a real-time face recognition system using Computer Vision and Deep Learning. The system is capable of detecting and recognizing faces in real-time using a webcam, as well as from images and video files. The project leverages OpenCV, dlib, and the face_recognition library to accurately detect, encode, and identify faces.
## 🔹 Features
1️⃣ **Real-Time Face Detection & Recognition**
Uses HOG (Histogram of Oriented Gradients) and CNN-based face detection for high accuracy.
Detects faces in real-time using a webcam.
Recognizes known faces by comparing them with stored encodings.
Can process multiple faces simultaneously in a frame.
2️⃣ **Face Encoding & Comparison**
Each face is converted into a 128-dimensional numerical encoding.
Uses Euclidean distance to compare detected faces with stored encodings.
Supports adding and removing faces from the database dynamically.
3️⃣ **High Accuracy & Performance**
Utilizes dlib’s deep learning-based face recognition model trained on a large dataset.
Works under different lighting conditions, facial angles, and expressions.
Supports face landmark detection for better facial analysis.
4️⃣ **OpenCV-Based Visualization**
Draws bounding boxes and labels detected faces in real-time.
Displays the name of recognized individuals on the screen.
Includes a confidence score to indicate recognition accuracy.
5️⃣ **Image & Video Processing Support**
Works with static images to detect and recognize faces.
Can process pre-recorded videos for face recognition.
Supports batch processing for analyzing multiple images at once.
6️⃣ **Face Data Storage & Management**
Stores face encodings in a database or local file system.
Allows adding new faces dynamically through a script or GUI.
Can be integrated with cloud storage for centralized face management.
7️⃣ **Scalability & Extensibility**
Can be extended to support:
Emotion recognition.
Age & gender detection.
Multiple cameras for large-scale deployments.
Integration with security systems & IoT devices.
## 🖥️ How It Works?
Face Detection: The system captures a video stream or loads an image.
Face Landmark Detection: Key facial features (eyes, nose, mouth, chin) are detected.
Face Encoding: Each detected face is converted into a 128-dimensional vector.
Face Matching: The encoded face is compared with stored encodings to identify individuals.
Label Display: If a match is found, the person’s name is displayed; otherwise, it is marked as Unknown.
## 📌 Applications
🔹 Security & Access Control – Face-based authentication for restricted access.
🔹 Attendance Systems – Automates attendance tracking in schools, offices, and events.
🔹 Smart Surveillance – Integrates with CCTV cameras for real-time monitoring.
🔹 Human-Computer Interaction – Enables hands-free user interaction in AI-driven applications.
🔹 Retail & Customer Insights – Identifies customers and provides personalized experiences.
🔧 **Future Enhancements**
✔️ Improve accuracy with CNN-based deep learning models.
✔️ Optimize performance for large-scale datasets with multi-threading.
✔️ Add support for face masking detection for COVID-19 compliance.
✔️ Implement age, gender, and emotion recognition using deep learning.
✔️ Integrate with cloud storage for storing and retrieving face data.
✔️ Develop a GUI-based application for easier user interaction.
