Step-by-Step Guide to Face Recognition Using MATLAB and Raspberry Pi

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When it comes to computer vision and face recognition, the first reaction is definitely to use OpenCV. Now with the latest MATLAB library, it can also be easily accomplished:

(Source: hackster.io) To implement the above face recognition function, the required software and hardware are also very simple:

Hardware: Raspberry Pi 3 model B, Raspberry Pi camera module

Software: MATLAB

Next, I will demonstrate step by step how to use MATLAB to achieve face recognition on Raspberry Pi:

About MATLAB, many people may think that it is a course in college, but in fact, many data researchers and artificial intelligence scientists use MATLAB. Although it may take some time to get started, once you get into it, you will find that MATLAB can basically accomplish all tasks. With the built-in tool library and related software support of MATLAB, you will be immersed and unable to extricate yourself. MATLAB even supports generating runnable software code for Raspberry Pi, which is a major wish for Raspberry Pi enthusiasts.

Installation

Before starting, make sure that MATLAB software is installed on your computer. I won’t go into detail about the installation of MATLAB, please solve it yourself.

Then, we need to install the relevant tool packages in MATLAB, the installation steps are:

1) Open MATLAB and select Add-Ons->Get Add-Ons

2) Search for ‘MATLAB Raspberry Pi support package’ -> install

3) Accept the terms

After the installation is complete, click ‘Run now’ to open the configuration assistant.

Configure Raspberry Pi to work with MATLAB

The principle of collaboration between MATLAB and Raspberry Pi is that MATLAB sends commands to Raspberry Pi while Raspberry Pi acts as a server in the project, interpreting and executing the received commands.

The development board setup assistant will guide you step by step to configure and install the image system designed for MATLAB on Raspberry Pi (Raspbian Image). Then the setup assistant will help you configure the image system step by step based on the selected network options.

Select Raspberry Pi model:

Select ‘Setup hardware with Mathworks Raspbian image’

Download the deep learning image file

Find the downloaded image file and unzip it:

Verify the image file

Burn the image

Find a memory card, connect it to the computer with a card reader, and start burning the image file.(Note: The MAC version of MATLAB cannot burn images. Friends using MAC can use ‘BalenaEtcher’ to burn the image.)

After burning the image, the next step is to establish a connection between MATLAB and Raspberry Pi.

There are four ways to connect Raspberry Pi and MATLAB:

1) Connect via LAN

2) Connect via wireless

3) Direct Ethernet connection

4) Manually configure network settings

◇ Select ‘connect to wireless network.’ Fill in the wireless SSID and password.Note that Raspberry Pi and PC need to be connected to the same wireless network.

◇ Select to obtain IP address automatically and click next.

Swipe to view steps

In general, MATLAB will set up the network connection on Raspberry Pi, but sometimes you also need to configure the Raspberry Pi’s WIFI network connection yourself.

◇ Connect a mouse, keyboard, and HDMI display to Raspberry Pi.

◇ Insert the burned SD card.

◇ Connect the 5v power supply.

◇ Connect the camera module to the Ethernet port of Raspberry Pi via the MIPI-CSI cable.

◇ Click ‘next’ on MATLAB to verify the connection.

◇ If the connection is successful, ‘test hardware connection successful’ will appear.Otherwise, ensure that your Raspberry Pi and PC are in the same network environment.

Transmit commands and control Raspberry Pi

If the test connection passes, you can now control Raspberry Pi through MATLAB.

In the MATLAB command window, enter:

‘mypi=raspi’

Then the relevant information of the Raspberry Pi development board will be feedback, including digital pins, number of LEDs, SPI channels, I2C channels, and rates in the MATLAB workspace.

Test whether the command is effective, enter in the command window:

writeLED(mypi,’led0’,1)

writeLED(mypi,’led0’,0)

To control the switch of the ‘ACT’ LED light on Raspberry Pi.

Connect the camera, enter in the command window

cam=cameraboard(mypi,’Resolution’,’1280×720’)

Run the face recognition program

Clear the test connection channel, enter in the MATLAB command window:

Clear

Point the Raspberry Pi camera at the face, create a new MATLAB script file, copy the code into the script file and name it facedetection.m. Make sure the current workspace is in the folder with the script file.

Open and run the facedetection.m program. This will open the ‘Figure-1’ window and start automatically detecting and recognizing faces. To stop, just click pause.

Next, you can also use the command fD=vision.CascadeObjectDetector to recognize noses, eyes, and ears. Of course, other objects such as cars, signs, and water bottles can also be recognized.

Code used:

%% set up your raspberry pi hardwareperipherals accessmypi = raspi();myCam = cameraboard(mypi)%% Image acquisition &amp; plottingmySnap = snapshot(myCam)imshow(mySnap)hold on%% Detect face and insert a rectangle around thefacefD = vision.CascadeObjectDetector(); %% fromcomputer vision toolboxbbox = step(fD, mySnap);imageOut = insertObjectAnnotation(mySnap,'rectangle', bbox, 'Face');imshow(imageOut);%% Repeat in a loop to make it look like facedetection in videoflag = 1;while flagclearvars -except mypi myCam flagmySnap = snapshot(myCam);imshow(mySnap);hold onfD = vision.CascadeObjectDetector();bbox = step(fD, mySnap);imageOut = insertObjectAnnotation(mySnap, 'rectangle',bbox, 'Face');imshow(imageOut);title('Detected Face');drawnow %% refreshes the image window afterevery loopend

The above is the complete process of using MATLAB and Raspberry Pi to achieve face recognition. If you have a Raspberry Pi, you might as well give it a try.

Currently, computer vision for face recognition still mostly uses OpenCV, the Darwin project practical training camp season 4: Computer Vision OpenCV is open! Every Monday/Wednesday, updates will be synchronized on the Darwin official website and Bilibili, if you want to quickly get started with OpenCV for computer vision, be sure to follow along~~

Click to follow the course

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