How to Create Face Recognition System Using Python ?

 

Step 1: Install Required Libraries

First, you'll need to install the necessary libraries. You can do this using pip:

bash
pip install opencv-python dlib face_recognition numpy

Step 2: Import Libraries

Next, import the necessary libraries in your Python script.

python
import cv2
import face_recognition
import numpy as np

Step 3: Load and Encode Faces

Load images of the faces you want to recognize and encode them. Encoding converts the facial features into a numerical representation that can be compared with other faces.

python
# Load a sample picture and learn how to recognize it.
image_of_person1 = face_recognition.load_image_file("person1.jpg")
person1_encoding = face_recognition.face_encodings(image_of_person1)[0]

# Load a second sample picture and learn how to recognize it.
image_of_person2 = face_recognition.load_image_file("person2.jpg")
person2_encoding = face_recognition.face_encodings(image_of_person2)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    person1_encoding,
    person2_encoding
]
known_face_names = [
    "Person 1",
    "Person 2"
]

Step 4: Initialize Video Capture

Set up video capture to use your webcam or a video file.

python
video_capture = cv2.VideoCapture(0)  # Use 0 for webcam, or replace with a video file path

Step 5: Recognize Faces in Video Stream

Process each frame from the video capture to recognize faces.

python
while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_frame = frame[:, :, ::-1]

    # Find all the faces and face encodings in the current frame of video
    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Loop through each face found in the frame
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        # See if the face is a match for the known faces
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

        name = "Unknown"

        # If a match was found in known_face_encodings, use the first one.
        if True in matches:
            first_match_index = matches.index(True)
            name = known_face_names[first_match_index]

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

Explanation:

1. Load and Encode Faces: Load images of the people you want to recognize and encode their faces.
2. Initialize Video Capture: Start capturing video from your webcam.
3. Recognize Faces in Video Stream: For each frame, find faces, encode them, compare with known faces, and display the results.

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