Monica Houston
Published © GPL3+

5 Minute Smart Doorbell

This project uses the MaaXBoard + Twilio to recognize faces and notify you who is at your door. And it only takes 5 minutes!

BeginnerFull instructions provided1 hour5,479
5 Minute Smart Doorbell

Things used in this project

Hardware components

Avnet MaaxBoard
Avnet 5v/3A USB Type-C power supply
Avnet MIPI-CSI Camera (or USB webcam)

Software apps and online services

SMS Messaging API
Twilio SMS Messaging API


Read more


# Modified from original demo here:

# Usage:
# Download photos of Obama and Biden to train the model (name them obama.jpg and biden.jpg)
# Invite them to your house, and surprise them by recognizing their faces! 
# (You can also replace these with photos of your friends)

import face_recognition
import cv2
import numpy as np
# Download the helper library from
from import Client

# Your Account Sid and Auth Token from
# DANGER! This is insecure. See
account_sid = '[YOUR ACCOUNT SID]'     # Edit this to be your account SID
auth_token = '[YOUR AUTH TOKEN]'        # Edit this to be your auth token
client = Client(account_sid, auth_token)

fromPhone = '[YOUR TWILIO NUMBER]'      # Edit this to be your twilio number
toPhone = '[NUMBER YOU WANT TO TEXT]'  # Edit this to be your own phone 

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

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

# Create arrays of known face encodings and their names
known_face_encodings = [
known_face_names = [
    "Barack Obama",
    "Joe Biden"

faceFound = 0
name = ""
prevName = ""

while True:
    if faceFound == 1 and name != prevName:
        print(name + " is at your door")
        message = client.messages \
                 body= name + " is at your door.",
    # Grab a single frame of video
    ret, frame =

    # 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 enqcodings in the 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 in this frame of video
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        # See if the face is a match for the known face(s)
        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

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

        # Or instead, use the known face with the smallest distance to the new face
        face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
        best_match_index = np.argmin(face_distances)
        if matches[best_match_index]:
            prevName = name
            name = known_face_names[best_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'):

# Release handle to the webcam


Monica Houston

Monica Houston

65 projects • 425 followers
I don't live on a boat anymore.