Microsoft Azure AI Vision SDK Samples

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This repository hosts sample code and setup documents for the Microsoft Azure AI Vision SDK (Preview).

Features

This repository hosts samples that help you get started with several features of the SDK in public preview. This includes the following API sets:

Other API sets are under development.

Support

Please open a new issue in this repo if you encounter any problems building or running the samples, or have any additional questions about the SDK. This is the preferred method of getting support. Note that these issues will be visible to the public, so please do not include any sensitive information.

Alternatively, you can contact Microsoft's Vision SDK development team directly by sending an e-mail to vision-sdk@microsoft.com.

Get the SDK samples

  • Running the samples in this repository requires you to install the Azure AI Vision SDK. By doing so you acknowledge the Azure AI Vision SDK license agreement.

  • The easiest way to get access to these samples is to download the content of this repo as a ZIP file.

  • Alternatively, you can use a Git client to clone this repository to your hard drive by running

    git clone https://github.com/Azure-Samples/azure-ai-vision-sdk.git

Image Analysis

January 2024 update: Samples for the new Image Analysis SDK versions 1.0.0-beta.1 and up are now located in several other Azure SDK GitHub repos. Please see the Image Analysis SDK overview page for more details.

Face Analysis

Overview

face See Microsoft documentation for an overview of Azure AI Vision Face Liveness Detection.

This SDK supports two feature variants:

  • Liveness with Verification
  • Liveness

Liveness detection aims to verify that the system engages with a physically present, living individual during the verification process. This is achieved by differentiating between a real (live) and fake (spoof) representation which may include photographs, videos, masks, or other means to mimic a real person.

The new Face liveness detection solution is a combination of mobile SDK and Azure AI service. It is conformant to ISO/IEC 30107-3 PAD (Presentation Attack Detection) standards as validated by iBeta level 1 and level 2 conformance testing. It successfully defends against a plethora of spoof types ranging from paper printouts, 2D/3D masks, and spoof presentations on phones and laptops. Liveness detection is an active area of research, with continuous improvements being made to counteract increasingly sophisticated spoofing attacks over time, and continuous improvement will be rolled out to the client and the service components as the overall solution gets hardened against new types of attacks over time.

While blocking spoof attacks is the primary focus of the liveness solution, paramount importance is also given to allowing real users to successfully pass the liveness check with low friction. Additionally, the liveness solution complies with the comprehensive responsible AI and data privacy standards to ensure fair usage across demographics around the world through extensive fairness testing. For more information, please visit: Empowering responsible AI practices | Microsoft AI.

Please see the readme documents listed below for instructions on how to build and run each sample.

Prerequisite

Samples

Sample Platform Description
Kotlin sample app for Android Android App with source code that demonstrates face analysis on Android
Swift sample app for iOS iOS App with source code that demonstrates face analysis on iOS

API Reference Documentation