In-person user identity verification is slow to scale, costly, and high friction for users. Machine learning (ML) powered facial recognition technology can enable online user identity verification. Amazon Rekognition offers pre-trained facial recognition capabilities that you can quickly add to your user onboarding and authentication workflows to verify opted-in users’ identities online. No ML expertise is required. With Amazon Rekognition, you can onboard and authenticate users in seconds while detecting fraudulent or duplicate accounts. As a result, you can grow users faster, reduce fraud, and lower user verification costs.
In this post, we describe a typical identity verification workflow and show how to build an identity verification solution using various Amazon Rekognition APIs. We provide a complete sample implementation in our GitHub repository.
User registration workflow
The following figure shows a sample workflow of a new user registration. Typical steps in this process are:

User captures selfie image

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