Case Studies | SaaS B2B

Automating identity checks with face liveness on AWS

Rekognition Identity Validation

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The challenge had three focus areas, all centred on making identity verification automatic, trustworthy and scalable.

Manual verification does not scale

Checking identity by hand is slow and inconsistent, and cannot keep pace as volumes grow across a distributed operation.

Telling a live person from a spoof

A simple face match is not enough. The system had to judge liveness, distinguishing a real person in front of the camera from a photo or a video.

A secure, scalable pipeline

Images had to be handled securely and the result returned in a clear, machine-readable form, on infrastructure that scales with demand rather than sitting idle.

Solution

An image is sent through an Amazon API Gateway endpoint and stored in an Amazon S3 bucket with least-privilege permissions. The API call triggers an AWS Lambda function, which retrieves the image from S3 and passes it to Amazon Rekognition.

Face

Confirms each image is a live person

JSON

Confidence score returned per image

Serverless

API-to-Lambda pipeline, scales on demand

By the numbers:

  • Face - Confirms each image is a live person
  • JSON - Confidence score returned per image
  • Serverless - API-to-Lambda pipeline, scales on demand
Changes

The proof of concept delivered an automated, serverless identity-verification pipeline in the client's AWS environment and met its success criteria. An image sent to the API is processed by Amazon Rekognition for liveness and returned as a JSON confidence score, replacing the manual check.

  • Automated verification — An image sent to the API is checked and scored automatically, with no manual review step.
  • Liveness, not just a face match — Amazon Rekognition judges whether the image is a real, live person rather than a photo or video.
  • A clear, machine-readable result — Each check returns a JSON confidence score, ready to plug into downstream decisions.
  • Secure and scalable — Images are stored in S3 with least-privilege permissions, and the serverless pipeline scales with demand.
  • Handover — Delivered in the client's AWS environment with a handover and a Well-Architected review to underpin further development.

With the liveness pipeline proven, the client has a scalable foundation to build automated identity verification into their wider processes, extending the same serverless pattern as volumes grow.

AWS Stack

Amazon Rekognition

For face liveness detection and identity confidence scoring.

AWS Lambda

For serverless processing of each image and returning the result.

Amazon API Gateway

For receiving images and returning the response through an API.

Amazon S3

For secure image storage and result output.

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