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To create a website and app with Amazon's facial recognition and ID document scanning capabilities, you can use Amazon Rekognition, which provides powerful image and video analysis features.

For facial recognition functionality, you'll need to:

1. Create a Rekognition Collection to store facial information. Collections act as server-side containers where you can store and search for facial data.

2. Implement a user registration flow that:

• Checks the quality of face images using the DetectFaces operation
• Compares input images to ID images using CompareFaces (if needed)
• Indexes faces using IndexFaces to store them in your Collection

3. Implement a verification/login flow that:

• Uses DetectFaces to ensure image quality
• Uses SearchFaces or SearchFacesByImage to find matching faces in your Collection

For best results with facial recognition:

• Ensure images are taken centrally with the face clearly visible
• Pay attention to lighting, image size, resolution, and backgrounds
• Consider implementing user vectors to improve search accuracy

When implementing these features, you'll need to:

• Set up AWS credentials and permissions
• Install the appropriate AWS SDK for your development platform (.NET, Python, etc.)
• Create the necessary backend services to handle the Rekognition API calls
• Build frontend components for capturing/uploading images

For ID document scanning, you can also use Amazon Rekognition's document analysis capabilities to extract information from identity documents.

Remember that facial recognition applications should be implemented with privacy considerations and appropriate user consent mechanisms.
Sources
Face Rekognition challenge | AWS re:Post
Using Amazon Rekognition for Identity Verification - Amazon Rekognition
Use SearchFaces with an AWS SDK or CLI - Amazon Rekognition
Detecting faces in an image - Amazon Rekognition