Welcome to our step-by-step tutorial on creating a Facial Recognition System powered by AWS Rekognition! In this video, we'll guide you through the entire process, from setting up the necessary infrastructure to deploying a functional web application using Docker.

Step 1: Setting Up Infrastructure
- Creating EC2 Instance: We'll start by launching an EC2 instance to serve as our computing environment.
- Configuring AWS CLI: Learn how to configure the AWS Command Line Interface for seamless interaction with AWS services.
- Creating AWS Rekognition Collection: We'll create a Rekognition collection to store and manage face data effectively.
- Cloning GitHub Repository: Get hands-on with cloning a GitHub repository containing essential resources for our project.

Step 2: Configuring Core Components
- Creating S3 Bucket: Set up an S3 bucket to store images and other resources.
- Creating DynamoDB Table: Learn how to create a DynamoDB table to store metadata and other relevant information.
- Creating Lambda Function: Discover the process of creating a Lambda function and configuring an S3 bucket as a trigger.

Step 3: Implementing Facial Recognition
- Uploading Images to S3: Upload images to the S3 bucket, triggering the Lambda function to generate face prints.
- Generating Face Prints: Witness the Lambda function in action as it generates face prints of uploaded images.
- Storing Face Prints in DynamoDB: Explore how face prints are uploaded to the DynamoDB table for efficient storage and retrieval.
- Running Web Application with Docker: Finally, we'll deploy a web application using Docker, showcasing the integration of our Facial Recognition System into a user-friendly interface.

Join us on this journey as we dive into the world of AWS Rekognition and build a powerful Facial Recognition System. Don't forget to like, share, and subscribe for more tutorials on AWS services and cutting-edge technologies!

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GitHub : https://github.com/ansarshaik965/Face...
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