The Auto Check-In App automatically provisions the products and services necessary to configure facial comparison and search for event check-in. As part of your event check-in process, you take a picture of event attendees, and the app sends them to Amazon Rekognition where the images are verified against a pre-registered attendee face collection.
The diagram below presents the architecture you can build using the example code on GitHub.
Auto Check-In App architecture
The code deploys an Amazon API Gateway, an Amazon Cognito user pool, AWS Lambda functions, Amazon DynamoDB tables, Amazon Rekognition, Amazon CloudWatch, and an Amazon Simple Storage Service (Amazon S3) bucket.
When an attendee registers for your event, they can upload a photo which is stored in an Amazon S3 bucket. The upload to Amazon S3 triggers a Lambda function that calls the Amazon Rekognition IndexFaces API. Amazon Rekognition extracts facial features into a feature vector and creates a face_id. Then, the vector is stored in a face collection, and the face_id and corresponding user name are stored in a DynamoDB table.
At the event, an operator uses the app’s Python-based UI and a camera to take a picture of the attendee, crop the photo, and send the photo to Amazon API Gateway which triggers a Lambda function that calls the Amazon Rekognition SearchFacesByImage API. Amazon Rekognition extracts the facial features from the image into a feature vector and compares the vector to the vectors in the face collection. When Amazon Rekognition finds a face with high similarity, the face_id is used to retrieve the user name. The user name is sent to the operator’s laptop showing that the attendee has been authenticated.
Once the attendee has been authenticated, the app automatically deletes the facial image the attendee uploaded during registration from Amazon S3. The photo taken at the event is not stored. It does not store any facial images after they are processed.
Easy to deploy
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