Face Recognition and Analysis System

This project provides a comprehensive real-time face recognition and facial analysis system using Python, OpenCV, Dlib, DeepFace, and the face_recognition library. The system detects faces, recognizes known individuals, and analyzes various facial attributes such as age, gender, emotions, and facial landmarks.

Features

• Face Detection: Detects faces in real-time using Dlib and face_recognition.
• Face Recognition: Identifies known faces by comparing them with pre-encoded face data.
• Facial Attribute Detection: Extracts attributes such as age, gender, emotion, and race using DeepFace.
• Facial Landmark Detection: Detects 68 facial landmarks using Dlib's shape predictor.
• Real-time Video Feed: Captures live video feed from a webcam and processes frames to display recognition and analysis results.
• Batch Image Processing: Processes images to detect, recognize, and analyze faces in static image files.

Example Usage

Processed Abdallah_Mohamed.jpeg: {'age': 23, 'gender': {'Woman': 0.00031815122838452226, 'Man': 99.99967813491821}, 'dominant_emotion': 'happy', 'dominant_race': 'middle eastern'}

Elon Musk: Age: 34 Dominant Emotion: Happy (97.22%) Dominant Gender: Man (99.73%) Dominant Race: Asian (33.31%) Confidence: 0.92 ...etc

Technologies

• OpenCV: Used for capturing video feed and image manipulation.
• Dlib: Used for detecting faces and predicting facial landmarks.
• DeepFace: Used for analyzing facial attributes such as age, gender, emotion, and race.
• Face Recognition Library: Used for encoding, comparing, and recognizing faces.

Prerequisites

To run the project, you will need to install the following libraries:

pip install opencv-python dlib face_recognition deepface

Ensure you have the shape_predictor_68_face_landmarks.dat file, which can be downloaded from Dlib's model page.

How to Run the Project

1. Clone the repository:

   git clone https://github.com/Abdallahelraey/Face-Recognition-and-Analysis-System.git

   cd "Face Recognition"

2. Prepare known faces:
   Place images of known individuals into the ./images/individuals directory. The system will automatically encode and store their facial data for later recognition.

3. Run the system:
   You can run the project in either static or real-time video mode by executing the main.py script. The system will prompt you to choose between these two modes.

   • Run the face recognition system:

     python main.py

   • The system will ask you to choose between:

     • static: To process a batch image.
     • video: To start real-time video recognition and analysis.

4. Real-time video face recognition and analysis:
   If you choose video, the system will start capturing video using your webcam. It will recognize and display known faces and their locations in real time.

   • Press q to quit the video feed.

5. Batch image face recognition and analysis:
   If you choose static, the system will load an image from the ./images/group/ directory (or any specified path) and perform face recognition and attribute analysis. The recognized faces, their locations, and their attributes will be printed in the console.

6. Run other modules directly: You can also run the two main face recognition modules directly without using main.py:

   • Real-time video recognition:

     python real_time_face_recognition_attributes_detention.py

   • Static image recognition:

     python static_face_recognition_attributes_detention.py

Project Structure

• main.py: The main entry point to choose between static and video face recognition.
• real_time_face_recognition_attributes_detention.py: Handles real-time video-based face recognition.
• static_face_recognition_attributes_detention.py: Handles batch image face recognition and analysis.
• ./images/individuals/: Directory to store images of known individuals for face recognition.
• ./images/group/: Directory to store group images or test images for batch recognition.

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Customization

You can modify the paths for images, tune the facial analysis settings in the DeepFace.analyze() method, or extend the system to include additional features like face tracking.

Example Usage

Real-Time Video Feed

The system will:

1. Detect and recognize faces from the live camera feed.
2. Display information about each face, such as name, age, gender, and emotions.
3. Annotate the video feed with detected facial landmarks.

Processed Abdallah_Mohamed.jpeg: {'age': 23, 'gender': {'Woman': 0.00031815122838452226, 'Man': 99.99967813491821}, 'dominant_emotion': 'happy', 'dominant_race': 'middle eastern'}

Contributing

If you wish to contribute to this project, please submit a pull request or open an issue.

License

This project is licensed under the Apache License.