Face Recognition with OpenCV python code

 import cv2

# Load the Haar Cascade face detection classifier

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Load the trained face recognition model

recognizer = cv2.face.LBPHFaceRecognizer_create()

recognizer.read('trained_model.xml')

# Set the video capture device (0 is usually the default webcam)

cap = cv2.VideoCapture(0)

while True:

    # Read a frame from the video stream

    ret, frame = cap.read()

    # Convert the frame to grayscale

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the grayscale frame

    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5)

    # Loop through each face detected

    for (x, y, w, h) in faces:

        # Crop the face region from the grayscale frame

        face_gray = gray[y:y+h, x:x+w]

        # Resize the face image to match the training image size

        face_gray = cv2.resize(face_gray, (100, 100))

        # Predict the label (person) of the face using the trained model

        label, confidence = recognizer.predict(face_gray)

        # Draw a rectangle around the face and display the predicted label

        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

        cv2.putText(frame, f'Person {label} ({confidence:.2f})', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    # Display the frame

    cv2.imshow('Face Recognition', frame)

    # Exit the loop if 'q' is pressed

    if cv2.waitKey(1) == ord('q'):

        break

# Release the video capture device and close the OpenCV window

cap.release()

cv2.destroyAllWindows()

 Note that this code assumes you have already trained a face recognition model and saved it to a file (in this case, trained_model.xml). If you haven't done this yet, you will need to train the model on a dataset of labeled face images before you can use it for recognition.