Explore face recognition

Note To complete this lab, you will need an Azure subscription in which you have administrative access.

Computer vision solutions often require an artificial intelligence (AI) solution to be able to detect human faces. For example, suppose the retail company Northwind Traders wants to locate where customers are standing in a store to best assist them. One way to accomplish this is to determine if there are any faces in the images, and if so, to identify the bounding box coordinates around the faces.

To test the capabilities of the Face service, we’ll use a simple command-line application that runs in the Cloud Shell. The same principles and functionality apply in real-world solutions, such as web sites or phone apps.

Create a Face API resource

You can use the Face service by creating a Face resource.

If you haven’t already done so, create a Face API resource in your Azure subscription.

  1. In another browser tab, open the Azure portal at https://portal.azure.com, signing in with your Microsoft account.

  2. Click the +Create a resource button, search for Face, and create a Face resource with the following settings:
    • Subscription: Your Azure subscription.
    • Resource group: Select or create a resource group with a unique name.
    • Region: Choose any available region.
    • Name: Enter a unique name.
    • Pricing tier: Free F0
  3. Review and create the resource, and wait for deployment to complete. Then go to the deployed resource.

  4. View the Keys and Endpoint page for your Face resource. You will need the endpoint and keys to connect from client applications.

Run Cloud Shell

To test the capabilities of the Face service, we’ll use a simple command-line application that runs in the Cloud Shell on Azure.

  1. In the Azure portal, select the [>_] (Cloud Shell) button at the top of the page to the right of the search box. This opens a Cloud Shell pane at the bottom of the portal.

    Start Cloud Shell by clicking on the icon to the right of the top search box

  2. The first time you open the Cloud Shell, you may be prompted to choose the type of shell you want to use (Bash or PowerShell). Select PowerShell. If you do not see this option, skip the step.

  3. If you are prompted to create storage for your Cloud Shell, ensure your subscription is specified and select Create storage. Then wait a minute or so for the storage to be created.

    Create storage by clicking confirm.

  4. Make sure the type of shell indicated on the top left of the Cloud Shell pane is switched to PowerShell. If it is Bash, switch to PowerShell by using the drop-down menu.

    How to find the left hand drop down menu to switch to PowerShell

  5. Wait for PowerShell to start. You should see the following screen in the Azure portal:

    Wait for PowerShell to start.

Configure and run a client application

Now that you have a custom model, you can run a simple client application that uses the Face service.

  1. In the command shell, enter the following command to download the sample application and save it to a folder called ai-900.

     git clone https://github.com/MicrosoftLearning/AI-900-AIFundamentals ai-900

    Tip If you already used this command in another lab to clone the ai-900 repository, you can skip this step.

  2. The files are downloaded to a folder named ai-900. Now we want to see all of the files in your Cloud Shell storage and work with them. Type the following command into the shell:

     code .

    Notice how this opens up an editor like the one in the image below:

    The code editor.

  3. In the Files pane on the left, expand ai-900 and select find-faces.ps1. This file contains some code that uses the Face service to detect and analyze faces in an image, as shown here:

    The editor containing code to detect faces in an image

  4. Don’t worry too much about the details of the code, the important thing is that it needs the endpoint URL and either of the keys for your Face resource. Copy these from the Keys and Endpoints page for your resource from the Azure portal and paste them into the code editor, replacing the YOUR_KEY and YOUR_ENDPOINT placeholder values respectively.

    Tip You may need to use the separator bar to adjust the screen area as you work with the Keys and Endpoint and Editor panes.

    After pasting the key and endpoint values, the first two lines of code should look similar to this:

  5. At the top right of the editor pane, use the button to open the menu and select Save to save your changes. Then open the menu again and select Close Editor.

    The sample client application will use your Face service to analyze the following image, taken by a camera in the Northwind Traders store:

    An image of a parent using a cellphone camera to take a picture of a child in in a store

  6. In the PowerShell pane, enter the following commands to run the code:

     cd ai-900
     ./find-faces.ps1 store-camera-1.jpg
  7. Review the returned information, which includes the location of the face in the image. The location of a face is indicated by the top-left coordinates, and the width and height of a bounding box, as shown here:

    An image of a person with their face outlined

    Note Face service capabilities that return personally identifiable features are restricted. See https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/ for details.

  8. Now let’s try another image:

    An image of person with a shopping basket

    To analyze the second image, enter the following command:

     ./find-faces.ps1 store-camera-2.jpg
  9. Review the results of the face analysis for the second image.

  10. Let’s try one more:

    An image of person with a shopping cart

    To analyze the third image, enter the following command:

     ./find-faces.ps1 store-camera-3.jpg
  11. Review the results of the face analysis for the third image.

Learn more

This simple app shows only some of the capabilities of the Face service. To learn more about what you can do with this service, see the Face API page.