☁️ AWS Lambda & Image Processing 🖼️

This project implements an AWS Lambda function in Python for facial image encoding. This function is designed to support mobile biometric authentication workflows by converting facial images into a biometric template.

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✨ Features

• Facial Image Encoding: Converts images into biometric templates for efficient processing.
• Serverless Environment: Optimized for deployment on AWS Lambda, ensuring a lightweight and scalable solution.
• Mobile Integration: Easily integrates into mobile authentication pipelines.
• Minimal Dependencies: Designed to be lightweight, with only essential libraries.

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🛠️ Tech Stack

• Python 3.x 🐍
• AWS Lambda
• AWS SDK (boto3)
• Image processing and encoding libraries (e.g., Pillow, face_recognition)

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📂 Project Structure

networkx/  (core algorithms and utilities)
tests/     (unit tests for validation)
utils/     (support functions)

Note: The folder structure is optimized for packaging into AWS Lambda layers or zipped deployments.

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🚀 Setup & Deployment

1. Install Dependencies

Install project dependencies into a local directory for packaging:

pip install -r requirements.txt -t ./package

2. Package for Lambda

Navigate to the package directory, create a zip file, and then add the main function file to the archive:

cd package
zip -r ../lambda_function.zip .
cd ..
zip -g lambda_function.zip lambda_function.py

3. Deploy to AWS

Upload the generated lambda_function.zip file to your AWS Lambda service. In the AWS Management Console, configure the handler to point to the main function.

lambda_function.lambda_handler

4. Testing

You can invoke the deployed Lambda function with a sample event containing a Base64-encoded image string:

{
  "image": "base64-encoded-image-string"
}

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✅ Use Cases

• Mobile Biometric Authentication
• Secure User Identity Verification
• Serverless, On-Demand Image Processing

👏 Credits Built by Arav Baboolal & Forage — 2025 🔥

For practice and educational use.