def run_face_recognition(database):
    """
    Start the face recognition via the webcam
    """
    # Open a handler for the camera
    video_capture = cv2.VideoCapture(CAMERA_DEVICE_ID)

    # the face_recognitino library uses keys and values of your database separately
    known_face_encodings = list(database.values())
    known_face_names = list(database.keys())

    while video_capture.isOpened():
        # Grab a single frame of video (and check if it went ok)
        ok, frame = video_capture.read()
        if not ok:
            logging.error("Could not read frame from camera. Stopping video capture.")
            break

        # run detection and embedding models
        face_locations, face_encodings = get_face_embeddings_from_image(frame, convert_to_rgb=True)

        # Loop through each face in this frame of video and see if there's a match
        for location, face_encoding in zip(face_locations, face_encodings):

            # get the distances from this encoding to those of all reference images
            distances = face_recognition.face_distance(known_face_encodings, face_encoding)

            # select the closest match (smallest distance) if it's below the threshold value
            if np.any(distances <= MAX_DISTANCE):
                best_match_idx = np.argmin(distances)
                name = known_face_names[best_match_idx]
            else:
                name = None

            # put recognition info on the image
            paint_detected_face_on_image(frame, location, name)

        # 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()