AWS-Powered Facial Recognition and Authorization System

Welcome to our detailed exploration of the AWS architecture powering our facial recognition and authorization system. This sophisticated system utilizes a range of AWS services to create a secure and scalable solution for managing both employee and visitor identities. By integrating tools like Amazon Rekognition, AWS Lambda, DynamoDB, and S3, we ensure efficient and reliable identity verification. Our architecture is designed to handle high volumes of data and adapt to varying workloads, making it a robust choice for modern identity management needs.

Architecture Overview

The architecture consists of two main flows:

1. Employee Registration:
   • Upload: Employee pictures are uploaded to an S3 bucket (S3: Employee pics).
   • Trigger: An event notification triggers a Lambda function (Registration Lambda).
   • Process: This Lambda function processes the image, extracts facial features using Amazon Rekognition, and stores the face ID, first name, and last name in a DynamoDB table.
2. Employee Authorization:
   • Upload: employee pictures are uploaded to another S3 bucket (S3: Visitor pics).
   • Trigger: An event notification triggers another Lambda function (Authorization Lambda).
   • Process: This Lambda function processes the image, retrieves facial features using Rekognition, and compares the face ID with data in DynamoDB. If a match is found, the relevant information is retrieved from DynamoDB or if match not found it will generate a message as person not found and stored as a text file in the visitor bucket.

Conclusion

We hope this detailed overview of our AWS architecture provides valuable insights into our facial recognition and authorization system. Contact us for more information or to see how we can help you implement a similar solution.