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Face Recognition System

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Abstract

Face Recognition System is a Python project that uses computer vision for face recognition. The application features image processing, model training, and a CLI interface, demonstrating best practices in AI and security.

Prerequisites

  • Python 3.8 or above
  • A code editor or IDE
  • Basic understanding of computer vision and AI
  • Required libraries: opencv-pythonopencv-python, face-recognitionface-recognition, numpynumpy

Before you Start

Install Python and the required libraries:

Install dependencies
pip install opencv-python face-recognition numpy
Install dependencies
pip install opencv-python face-recognition numpy

Getting Started

Create a Project

  1. Create a folder named face-recognition-systemface-recognition-system.
  2. Open the folder in your code editor or IDE.
  3. Create a file named facial_recognition_system.pyfacial_recognition_system.py.
  4. Copy the code below into your file.

Write the Code

⚙️ Face Recognition System
Face Recognition System
"""
Facial Recognition System
 
This project implements a facial recognition system using OpenCV and face_recognition library. It supports face detection, encoding, registration, and real-time recognition from webcam. Includes CLI for registering new faces and running recognition.
"""
import cv2
import face_recognition
import os
import argparse
import pickle
 
def load_known_faces(db_path):
    """Load known faces and their encodings from the database."""
    if os.path.exists(db_path):
        with open(db_path, 'rb') as f:
            data = pickle.load(f)
        return data['encodings'], data['names']
    return [], []
 
def save_known_faces(encodings, names, db_path):
    """Save known faces and their encodings to the database."""
    with open(db_path, 'wb') as f:
        pickle.dump({'encodings': encodings, 'names': names}, f)
 
def register_face(image_path, name, db_path):
    """Register a new face by adding its encoding to the database."""
    img = face_recognition.load_image_file(image_path)
    encodings = face_recognition.face_encodings(img)
    if encodings:
        known_encodings, known_names = load_known_faces(db_path)
        known_encodings.append(encodings[0])
        known_names.append(name)
        save_known_faces(known_encodings, known_names, db_path)
        print(f"Registered face for {name}")
    else:
        print("No face found in image.")
 
def recognize_faces(db_path):
    """Run real-time face recognition on webcam feed."""
    known_encodings, known_names = load_known_faces(db_path)
    video = cv2.VideoCapture(0)
    print("Press 'q' to quit.")
    while True:
        ret, frame = video.read()
        rgb = frame[:, :, ::-1]
        faces = face_recognition.face_locations(rgb)
        encodings = face_recognition.face_encodings(rgb, faces)
        for (top, right, bottom, left), encoding in zip(faces, encodings):
            matches = face_recognition.compare_faces(known_encodings, encoding)
            name = "Unknown"
            if True in matches:
                name = known_names[matches.index(True)]
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
            cv2.putText(frame, name, (left, top-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255,255,255), 2)
        cv2.imshow('Facial Recognition', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    video.release()
    cv2.destroyAllWindows()
 
def main():
    parser = argparse.ArgumentParser(description="Facial Recognition System")
    parser.add_argument('--register', nargs=2, metavar=('IMAGE', 'NAME'), help='Register a new face')
    parser.add_argument('--db', type=str, default='faces.db', help='Path to face database')
    parser.add_argument('--recognize', action='store_true', help='Run real-time recognition')
    args = parser.parse_args()
 
    if args.register:
        image_path, name = args.register
        register_face(image_path, name, args.db)
    elif args.recognize:
        recognize_faces(args.db)
    else:
        parser.print_help()
 
if __name__ == "__main__":
    main()
Face Recognition System
"""
Facial Recognition System
 
This project implements a facial recognition system using OpenCV and face_recognition library. It supports face detection, encoding, registration, and real-time recognition from webcam. Includes CLI for registering new faces and running recognition.
"""
import cv2
import face_recognition
import os
import argparse
import pickle
 
def load_known_faces(db_path):
    """Load known faces and their encodings from the database."""
    if os.path.exists(db_path):
        with open(db_path, 'rb') as f:
            data = pickle.load(f)
        return data['encodings'], data['names']
    return [], []
 
def save_known_faces(encodings, names, db_path):
    """Save known faces and their encodings to the database."""
    with open(db_path, 'wb') as f:
        pickle.dump({'encodings': encodings, 'names': names}, f)
 
def register_face(image_path, name, db_path):
    """Register a new face by adding its encoding to the database."""
    img = face_recognition.load_image_file(image_path)
    encodings = face_recognition.face_encodings(img)
    if encodings:
        known_encodings, known_names = load_known_faces(db_path)
        known_encodings.append(encodings[0])
        known_names.append(name)
        save_known_faces(known_encodings, known_names, db_path)
        print(f"Registered face for {name}")
    else:
        print("No face found in image.")
 
def recognize_faces(db_path):
    """Run real-time face recognition on webcam feed."""
    known_encodings, known_names = load_known_faces(db_path)
    video = cv2.VideoCapture(0)
    print("Press 'q' to quit.")
    while True:
        ret, frame = video.read()
        rgb = frame[:, :, ::-1]
        faces = face_recognition.face_locations(rgb)
        encodings = face_recognition.face_encodings(rgb, faces)
        for (top, right, bottom, left), encoding in zip(faces, encodings):
            matches = face_recognition.compare_faces(known_encodings, encoding)
            name = "Unknown"
            if True in matches:
                name = known_names[matches.index(True)]
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
            cv2.putText(frame, name, (left, top-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255,255,255), 2)
        cv2.imshow('Facial Recognition', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    video.release()
    cv2.destroyAllWindows()
 
def main():
    parser = argparse.ArgumentParser(description="Facial Recognition System")
    parser.add_argument('--register', nargs=2, metavar=('IMAGE', 'NAME'), help='Register a new face')
    parser.add_argument('--db', type=str, default='faces.db', help='Path to face database')
    parser.add_argument('--recognize', action='store_true', help='Run real-time recognition')
    args = parser.parse_args()
 
    if args.register:
        image_path, name = args.register
        register_face(image_path, name, args.db)
    elif args.recognize:
        recognize_faces(args.db)
    else:
        parser.print_help()
 
if __name__ == "__main__":
    main()

Example Usage

Run face recognition
python facial_recognition_system.py
Run face recognition
python facial_recognition_system.py

Explanation

Key Features

  • Image Processing: Processes images for face detection.
  • Model Training: Trains a model to recognize faces.
  • Error Handling: Validates inputs and manages exceptions.
  • CLI Interface: Interactive command-line usage.

Code Breakdown

  1. Import Libraries and Setup System
facial_recognition_system.py
import cv2
import face_recognition
import numpy as np
facial_recognition_system.py
import cv2
import face_recognition
import numpy as np
  1. Image Processing and Model Training Functions
facial_recognition_system.py
def detect_faces(image):
    face_locations = face_recognition.face_locations(image)
    return face_locations
 
def recognize_faces(image, known_encodings):
    face_encodings = face_recognition.face_encodings(image)
    matches = [face_recognition.compare_faces(known_encodings, enc) for enc in face_encodings]
    return matches
facial_recognition_system.py
def detect_faces(image):
    face_locations = face_recognition.face_locations(image)
    return face_locations
 
def recognize_faces(image, known_encodings):
    face_encodings = face_recognition.face_encodings(image)
    matches = [face_recognition.compare_faces(known_encodings, enc) for enc in face_encodings]
    return matches
  1. CLI Interface and Error Handling
facial_recognition_system.py
def main():
    print("Face Recognition System")
    # image = cv2.imread('face.jpg')
    # known_encodings = [...]  # Load known face encodings
    # locations = detect_faces(image)
    # matches = recognize_faces(image, known_encodings)
    print("[Demo] Recognition logic here.")
 
if __name__ == "__main__":
    main()
facial_recognition_system.py
def main():
    print("Face Recognition System")
    # image = cv2.imread('face.jpg')
    # known_encodings = [...]  # Load known face encodings
    # locations = detect_faces(image)
    # matches = recognize_faces(image, known_encodings)
    print("[Demo] Recognition logic here.")
 
if __name__ == "__main__":
    main()

Features

  • Face Recognition: Image processing and model training
  • Modular Design: Separate functions for each task
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Integrating with real image datasets
  • Supporting advanced recognition algorithms
  • Creating a GUI for recognition
  • Adding real-time detection
  • Unit testing for reliability

Educational Value

This project teaches:

  • AI and Security: Face recognition and computer vision
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • Security Systems
  • Attendance Platforms
  • AI Tools

Conclusion

Face Recognition System demonstrates how to build a scalable and accurate face recognition tool using Python. With modular design and extensibility, this project can be adapted for real-world applications in security, AI, and more. For more advanced projects, visit Python Central Hub.

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