Introduction
Having a smart doorbell with facial recognition can be a great way to enhance your home's security and convenience. With this DIY project, I will show you how to build your own smart doorbell using a Raspberry Pi and OpenCV. This will allow you to create a doorbell that can identify familiar faces, notify you when someone is at the door, and even unlock the door for family and friends.
Hardware Needed
To build this DIY facial recognition doorbell, you will need the following hardware components:
Raspberry Pi
The Raspberry Pi is the heart of this project. I recommend using a Raspberry Pi 4 for the best performance. Any Raspberry Pi model with wireless connectivity and at least 1GB of RAM should work though.
Camera Module
You need an official Raspberry Pi camera module or a compatible camera like the Arducam to capture images of visitors. Make sure it is compatible with your Raspberry Pi model.
MicroSD Card
You need a MicroSD card with at least 8GB of capacity to store the operating system and software for the Raspberry Pi. A class 10 card is recommended for the best performance.
Power Supply
A 5V USB power adapter with at least 3A output is required to power the Raspberry Pi. An official Raspberry Pi power supply is recommended.
Jumper Wires
Jumper wires are needed to connect components to the Raspberry Pi GPIO pins. Male-to-female jumper wires work best.
Doorbell Button
A simple doorbell button can be wired up to the Raspberry Pi to act as the doorbell actuator. Any momentary push button switch will work.
Relay Module (optional)
A relay module allows you to control a higher voltage electric door lock from the Raspberry Pi. This is optional if you just want notifications.
Speaker (optional)
Adding a speaker allows you to play custom audio notifications when someone rings the doorbell. Any USB speaker or 3.5mm audio jack speaker will work.
Software Required
You will need the following software installed on your Raspberry Pi:
- Raspberry Pi OS - The official operating system for Raspberry Pi.
- OpenCV - The open source computer vision library used for facial recognition.
- NumPy - Adds support for OpenCV matrix operations.
- Face Recognition - A Python face recognition module.
- Flask - A Python web framework for building the web interface.
I recommend installing these packages through the terminal on Raspberry Pi OS for the best compatibility. You can also use Docker containers for simplified installation.
How to Assemble the Hardware
Here are the steps to assemble the hardware components:
1. Install the Camera Module
Attach the camera module to the CSI camera port on your Raspberry Pi. Make sure it is properly seated and secured.
2. Insert the MicroSD Card
Insert the MicroSD card loaded with Raspberry Pi OS into the card slot on the Raspberry Pi.
3. Connect the Speaker and Doorbell Button
Use jumper wires to connect the doorbell button and optional speaker to the GPIO pins on the Raspberry Pi. Refer to the pinout diagram for your Pi model.
4. Power on the Raspberry Pi
Connect the power supply to your Pi to power it on. Once booted, it will connect to your WiFi network automatically.
5. Test the Camera
Run raspistill -o test.jpg
to take a test photo and validate the camera module is working.
Now the hardware is ready! Next we'll install and configure the software.
Installing the Required Software
With the hardware assembled, we can install the operating system and required software:
1. Install Raspberry Pi OS
Burn the Raspberry Pi OS image to your MicroSD card. I recommend the 32-bit Lite version for best performance.
2. Boot and Connect Raspberry Pi
Insert the microSD card and power on your Pi. It should automatically connect to your WiFi. Log in remotely through SSH.
3. Update Packages
Run sudo apt update && sudo apt full-upgrade
to update packages to latest versions. Reboot after updating.
4. Install OpenCV
Use this command to install OpenCV:
sudo apt install libopencv-dev python3-opencv
Verify it installed with python3 -c "import cv2; print(cv2.__version__)"
5. Install Face Recognition
pip3 install face_recognition
Test with:
python3 -c "import face_recognition"
6. Install Other Packages
sudo apt install flask numpy
The software is now ready to use!
Capturing and Processing Face Images with OpenCV
To recognize faces, we first need to capture and pre-process some images of familiar people using OpenCV:
1. Capture Images
Use raspistill
to capture images of faces. Try to get 5-10 good quality images per person with variations.
2. Detect Faces
Load images in OpenCV using cv2.imread()
then detect faces with cv2.CascadeClassifier()
.
3. Extract Face Regions
Use cv2.boundingRect()
on detected faces to extract the face regions.
4. Resize Faces
Resize the extracted faces to 50x50 pixels using cv2.resize()
.
5. Encode Faces
Encode the resized face images to 128-d vectors with face_recognition.face_encodings()
.
This gives us the encoded arrays needed for facial recognition. We'll use these in our doorbell script.
Developing the Smart Doorbell Script
With images processed, we can now develop the facial recognition doorbell script:
1. Import Modules
Import required modules:
python
import face_recognition
import cv2
from flask import Flask
2. Initialize Flask App
Initialize a Flask app to receive camera snapshots:
python
app = Flask(__name__)
3. Load Face Encodings
Load pre-calculated face encodings from processed images.
4. Start Video Stream
Start streaming video from the camera module using OpenCV.
5. Read Video Frames
Continuously read frames from the video stream.
6. Detect Faces
Detect faces in each frame using OpenCV's classifier.
7. Encode Detected Faces
Generate encodings for the detected faces using face_recognition
.
8. Recognize Faces
Compare detected face encodings to known people encodings. Identify face matches.
9. Perform Actions
If a recognized face is detected, perform any actions like sending notifications, unlocking door, etc.
10. Stream Image to Flask
Stream the current image to Flask to display in the web interface.
This completes the core logic. Now we can work on the web interface.
Creating the Facial Recognition Doorbell Web Interface
To view the video feed and facial recognition results, we'll build a simple web interface with Flask:
1. Create Flask Routes
Define Flask router functions like:
python
@app.route('/')
def home():
return render_template('home.html')
2. Call Doorbell Script
When the web interface loads, run our facial recognition script in the background.
3. Display Video Feed
Display the streaming video feed from the camera module.
4. List Recognized Faces
Show the names and times of each recognized face.
5. Show Buttons
Add interface buttons like "Reload", "Unlock Door", etc.
6. Style Template
Use CSS to style and layout the web template nicely.
7. Run Flask App
Run the Flask app on your network:
bash
flask run --host=0.0.0.0
Now we have a fully functioning web based smart doorbell!
Installing the Doorbell Hardware
To install your new facial recognition doorbell:
-
Mount the camera module near your door pointing outward.
-
Attach the doorbell button outside using wires through a small hole.
-
Connect or hold the Raspberry Pi and an optional speaker inside.
-
Power on the Pi and test everything is working before mounting it.
-
Access the web interface on your phone or computer using the Pi's IP address.
With that, you can now monitor your doorbell webcam, see recognized faces, and customize notifications and responses!
Conclusion
Building your own AI smart doorbell with Raspberry Pi and OpenCV takes some effort, but provides a very cool and useful security upgrade to your home. With the steps in this guide, you can create a system that recognizes friends and family members arriving at your door. Some additional ideas for enhancing your smart doorbell include sending mobile push notifications, integrating with smart speakers for voice notifications, and adding secure remote access. The possibilities are endless!