Merge pull request #266455 from MicrosoftDocs/main

02/15 PM Publishing

Author: v-alje

articles/active-directory-b2c/media/b2c-global-identity-funnel-based-design/local-federated-account-merge.png

@@ -10,28 +10,31 @@ ms.subservice: azure-ai-face
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 11/17/2020
+ms.date: 02/14/2024
 ms.author: pafarley
 ---
 
 # Build a React Native app to add users to a Face service
 
-This guide will show you how to get started with the sample Face enrollment application. The app demonstrates best practices for obtaining meaningful consent to add users into a face recognition service and acquire high-accuracy face data. An integrated system could use an app like this to provide touchless access control, identification, attendance tracking, or personalization kiosk, based on their face data.
+This guide will show you how to get started with a sample Face enrollment application. The app demonstrates best practices for obtaining meaningful consent to add users into a face recognition service and acquire high-quality face data. An integrated system could use an app like this to provide touchless access control, identification, attendance tracking, or personalization kiosk, based on their face data.
 
-When launched, the application shows users a detailed consent screen. If the user gives consent, the app prompts for a username and password and then captures a high-quality face image using the device's camera.
+When users launch the app, it shows a detailed consent screen. If the user gives consent, the app prompts them for a username and password and then captures a high-quality face image using the device's camera.
 
-The sample app is written using JavaScript and the React Native framework. It can currently be deployed on Android and iOS devices; more deployment options are coming in the future.
+The sample app is written using JavaScript and the React Native framework. It can be deployed on Android and iOS devices.
 
 ## Prerequisites 
 
 * An Azure subscription – [Create one for free](https://azure.microsoft.com/free/cognitive-services/).  
 * Once you have your Azure subscription, [create a Face resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesFace) in the Azure portal to get your key and endpoint. After it deploys, select **Go to resource**.  
   * You'll need the key and endpoint from the resource you created to connect your application to Face API.  
 
-### Important Security Considerations
-* For local development and initial limited testing, it is acceptable (although not best practice) to use environment variables to hold the API key and endpoint. For pilot and final deployments, the API key should be stored securely - which likely involves using an intermediate service to validate a user token generated during login. 
-* Never store the API key or endpoint in code or commit them to a version control system (e.g. Git). If that happens by mistake, you should immediately generate a new API key/endpoint and revoke the previous ones.
-* As a best practice, consider having separate API keys for development and production.
+
+> [!IMPORTANT]
+> **Security considerations**
+>
+> * For local development and initial limited testing, it is acceptable (although not best practice) to use environment variables to hold the API key and endpoint. For pilot and final deployments, the API key should be stored securely - which likely involves using an intermediate service to validate a user token generated during login. 
+> * Never store the API key or endpoint in code or commit them to a version control system (e.g. Git). If that happens by mistake, you should immediately generate a new API key/endpoint and revoke the previous ones.
+> * As a best practice, consider having separate API keys for development and production.
 
 ## Set up the development environment
 
@@ -63,7 +66,7 @@ The sample app is written using JavaScript and the React Native framework. It ca
 
 ## Customize the app for your business   
 
-Now that you have set up the sample app, you can tailor it to your own needs.
+Now that you've set up the sample app, you can tailor it to your own needs.
 
 For example, you may want to add situation-specific information on your consent page:
 
@@ -76,7 +79,7 @@ For example, you may want to add situation-specific information on your consent
     * Face size (faces that are distant from the camera)
     * Face orientation (faces turned or tilted away from camera)
     * Poor lighting conditions (either low light or backlighting) where the image may be poorly exposed or have too much noise
-    * Occlusion (partially hidden or obstructed faces) including accessories like hats or thick-rimmed glasses)
+    * Occlusion (partially hidden or obstructed faces), including accessories like hats or thick-rimmed glasses
     * Blur (such as by rapid face movement when the photograph was taken). 
 
     The service provides image quality checks to help you make the choice of whether the image is of sufficient quality based on the above factors to add the customer or attempt face recognition. This app demonstrates how to access frames from the device's camera, detect quality and show user interface messages to the user to help them capture a higher quality image, select the highest-quality frames, and add the detected face into the Face API service. 

articles/active-directory-b2c/media/b2c-global-identity-funnel-based-design/traveling-federated-user-merge-account.png

@@ -7,7 +7,7 @@ author: PatrickFarley
 manager: nitinme
 ms.service: azure-ai-vision
 ms.topic: tutorial 
-ms.date: 12/29/2022
+ms.date: 02/14/2024
 ms.author: pafarley
 ms.devlang: csharp
 ms.custom: devx-track-csharp, build-2023, build-2023-dataai
@@ -411,38 +411,38 @@ Next, you'll add the code that actually uses the Azure AI Vision service to crea
 1. Open the *HomeController.cs* file in the project's **Controllers** folder and add the following `using` statements at the top of the file:
 
     ```csharp
-    using Azure.AI.Vision.Common;
+    using Azure;
     using Azure.AI.Vision.ImageAnalysis;
+    using System;
     ```
 
 1. Then, go to the **Upload** method; this method converts and uploads images to blob storage. Add the following code immediately after the block that begins with `// Generate a thumbnail` (or at the end of your image-blob-creation process). This code takes the blob containing the image (`photo`), and uses Azure AI Vision to generate a description for that image. The Azure AI Vision API also generates a list of keywords that apply to the image. The generated description and keywords are stored in the blob's metadata so that they can be retrieved later on.
 
     ```csharp
-    // Submit the image to the Azure AI Vision API
-    var serviceOptions = new VisionServiceOptions(
-        Environment.GetEnvironmentVariable(ConfigurationManager.AppSettings["VisionEndpoint"]),
+    // create a new ImageAnalysisClient
+    ImageAnalysisClient client = new ImageAnalysisClient(
+            new Uri(Environment.GetEnvironmentVariable(ConfigurationManager.AppSettings["VisionEndpoint"])),
             new AzureKeyCredential(ConfigurationManager.AppSettings["SubscriptionKey"]));
 
-    var analysisOptions = new ImageAnalysisOptions()
-        {
-            Features = ImageAnalysisFeature.Caption | ImageAnalysisFeature.Tags,
-            Language = "en",
-            GenderNeutralCaption = true
-        };
+    VisualFeatures = visualFeatures = VisualFeatures.Caption | VisualFeatures.Tags;
 
-    using var imageSource = VisionSource.FromUrl(
-        new Uri(photo.Uri.ToString()));
+    ImageAnalysisOptions analysisOptions = new ImageAnalysisOptions()
+    {
+        GenderNeutralCaption = true,
+        Language = "en",
+    };
 
-    using var analyzer = new ImageAnalyzer(serviceOptions, imageSource, analysisOptions);
-    var result = analyzer.Analyze();
+    Uri imageURL = new Uri(photo.Uri.ToString());
+    
+    ImageAnalysisResult  result = client.Analyze(imageURL,visualFeatures,analysisOptions);
 
     // Record the image description and tags in blob metadata
-    photo.Metadata.Add("Caption", result.Caption.ContentCaption.Content);
+    photo.Metadata.Add("Caption", result.Caption.Text);
 
-    for (int i = 0; i < result.Tags.ContentTags.Count; i++)
+    for (int i = 0; i < result.Tags.Values.Count; i++)
     {
         string key = String.Format("Tag{0}", i);
-        photo.Metadata.Add(key, result.Tags.ContentTags[i]);
+        photo.Metadata.Add(key, result.Tags.Values[i]);
     }
 
     await photo.SetMetadataAsync();
@@ -554,7 +554,7 @@ In this section, you will add a search box to the home page, enabling users to d
     }
     ```
 
-    Observe that the **Index** method now accepts a parameter _id_ that contains the value the user typed into the search box. An empty or missing _id_ parameter indicates that all the photos should be displayed.
+    Observe that the **Index** method now accepts a parameter `id` that contains the value the user typed into the search box. An empty or missing `id` parameter indicates that all the photos should be displayed.
 
 1. Add the following helper method to the **HomeController** class:
 

articles/active-directory-b2c/media/b2c-global-identity-funnel-based-design/traveling-user-account-signin.png

@@ -70,7 +70,7 @@ Attributes are a set of features that can optionally be detected by the [Face -
     >[!NOTE]
     > The availability of each attribute depends on the detection model specified. QualityForRecognition attribute also depends on the recognition model, as it is currently only available when using a combination of detection model detection_01 or detection_03, and recognition model recognition_03 or recognition_04.
 
-## Input data
+## Input requirements
 
 Use the following tips to make sure that your input images give the most accurate detection results:
 

articles/active-directory-b2c/media/b2c-global-identity-funnel-based-design/traveling-user-forgot-password.png

@@ -11,13 +11,13 @@ ms.subservice: azure-ai-face
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 12/27/2022
+ms.date: 02/14/2024
 ms.author: pafarley
 ---
 
 # Face recognition
 
-This article explains the concept of Face recognition, its related operations, and the underlying data structures. Broadly, face recognition is the act of verifying or identifying individuals by their faces. Face recognition is important in implementing the identification scenario, which enterprises and apps can use to verify that a (remote) user is who they claim to be.
+This article explains the concept of Face recognition, its related operations, and the underlying data structures. Broadly, face recognition is the process of verifying or identifying individuals by their faces. Face recognition is important in implementing the identification scenario, which enterprises and apps can use to verify that a (remote) user is who they claim to be.
 
 You can try out the capabilities of face recognition quickly and easily using Vision Studio.
 > [!div class="nextstepaction"]
@@ -48,7 +48,7 @@ The recognition operations use mainly the following data structures. These objec
 
 See the [Face recognition data structures](./concept-face-recognition-data-structures.md) guide.
 
-## Input data
+## Input requirements
 
 Use the following tips to ensure that your input images give the most accurate recognition results:
 

articles/active-directory-b2c/media/b2c-global-identity-solutions/b2c-architecture.png

@@ -8,7 +8,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 05/03/2023
+ms.date: 02/14/2024
 ms.author: pafarley
 ms.custom: references_regions, build-2023, build-2023-dataai
 ---
@@ -35,13 +35,13 @@ Try out the capabilities of Product Recognition quickly and easily in your brows
 
 ## Product Recognition features
 
-### Shelf Image Composition
+### Shelf image composition
 
 The [stitching and rectification APIs](./how-to/shelf-modify-images.md) let you modify images to improve the accuracy of the Product Understanding results. You can use these APIs to:
 * Stitch together multiple images of a shelf to create a single image.
 * Rectify an image to remove perspective distortion.
 
-### Shelf Product Recognition (pretrained model)
+### Shelf product recognition (pretrained model)
 
 The [Product Understanding API](./how-to/shelf-analyze.md) lets you analyze a shelf image using the out-of-box pretrained model. This operation detects products and gaps in the shelf image and returns the bounding box coordinates of each product and gap, along with a confidence score for each.
 
@@ -90,7 +90,7 @@ The following JSON response illustrates what the Product Understanding API retur
 }
 ```
 
-### Shelf Product Recognition - Custom (customized model)
+### Shelf product recognition (customized model)
 
 The Product Understanding API can also be used with a [custom trained model](./how-to/shelf-model-customization.md) to detect your specific products. This operation returns the bounding box coordinates of each product and gap, along with the label of each product.
 
@@ -139,7 +139,7 @@ The following JSON response illustrates what the Product Understanding API retur
 }
 ```
 
-### Shelf Planogram Compliance (preview)
+### Shelf planogram compliance
 
 The [Planogram matching API](./how-to/shelf-planogram.md) lets you compare the results of the Product Understanding API to a planogram document. This operation matches each detected product and gap to its corresponding position in the planogram document.
 
@@ -183,3 +183,4 @@ It returns a JSON response that accounts for each position in the planogram docu
 Get started with Product Recognition by trying out the stitching and rectification APIs. Then do basic analysis with the Product Understanding API.
 * [Prepare images for Product Recognition](./how-to/shelf-modify-images.md)
 * [Analyze a shelf image](./how-to/shelf-analyze.md)
+* [API reference](https://eastus.dev.cognitive.microsoft.com/docs/services/unified-vision-apis-public-preview-2023-04-01-preview/operations/644aba14fb42681ae06f1b0b)