AWS Face Comparison

A Python implementation for comparing faces between two images using AWS Rekognition. This tool provides detailed similarity analysis and face position information.

Prerequisites

• Python 3.8.8
• AWS Account with Rekognition access
• AWS Access Key and Secret Key
• boto3 library

Installation

pip install boto3

Required Libraries

import boto3
from botocore.exceptions import ClientError, WaiterError

Configuration

aws_accesskey = "Your Access Key"
aws_secretaccess = "Your Secret Access Key"
myregion = "your-region"

Main Function

def Compare_Faces(aws_access, aws_secret, aws_region, Source_Image, Target_Image):
    """
    Compares faces between two images.
    
    Args:
        aws_access: AWS access key
        aws_secret: AWS secret key
        aws_region: AWS region name
        Source_Image: Path to source image file
        Target_Image: Path to target image file
        
    Returns:
        int: Number of matching faces found
        
    Prints:
        Position and similarity percentage for each match
    """

Features

1. Face Comparison:

   • Similarity threshold set to 80%
   • Returns position data for matched faces
   • Provides similarity confidence score
   • Handles multiple faces in images

2. Resource Management:

   • Proper file handling with close operations
   • Error handling for API calls
   • Memory efficient image processing

Usage Example

matches = Compare_Faces(
    aws_accesskey,
    aws_secretaccess,
    "us-west-2",
    "source_image.jpg",
    "target_image.jpg"
)

Output Format

Successful Match

The face at 0.229 0.076 matches with 99.99% confidence

No Match

Returns 0 (no output printed)

Response Interpretation

1. Matching Faces:

   • Returns number of matches found (>0)
   • Provides position coordinates
   • Shows similarity percentage

2. Non-Matching Faces:

   • Returns 0
   • No position data printed
   • Indicates no matches above threshold

Best Practices

1. Image Quality:

   • Use clear, well-lit images
   • Ensure faces are clearly visible
   • Consider image resolution
   • Minimize face occlusion

2. Error Handling:

   • Check file existence
   • Validate image formats
   • Handle API limits
   • Manage file resources

3. Performance:

   • Close file handles properly
   • Consider image size optimization
   • Monitor API usage

Limitations

1. Similarity Threshold:

   • Fixed at 80%
   • Matches below threshold ignored
   • May need adjustment for specific use cases

2. Image Requirements:

   • Must be readable binary files
   • Faces must be detectable
   • Limited by AWS Rekognition constraints

Error Cases to Handle

1. File Operations:

   • File not found
   • Permission issues
   • Invalid file formats

2. API Related:

   • Network errors
   • Service limits
   • Authentication failures