This serverless project uses Amazon Rekognition for real-time missing person detection. Users can upload images (photos or CCTV frames),fill a few form details and the system matches them with a database of missing persons.
The goal is to automate face-matching, reducing delays and human error, enabling fast action by citizens and authorities.
- Real-Time Face Matching: Upload an image or CCTV snapshot to get instant results via Amazon Rekognition.
- Enhanced Upload Form (NEW): Collects detailed information like time, location, and description to aid in analysis.
- Fast & Accurate: Eliminates manual search with AI-powered facial comparison.
- Serverless Architecture: Scales seamlessly using AWS Lambda, API Gateway, S3, CloudFront and Route 53.
- Civilians browse websites and manually compare faces.
- Time-consuming, error-prone, and inefficient.
- Upload a single image, and fill few form details.
- Automatically match against missing persons.
- Instant, accurate, and user-friendly, also stores user data via Formspree.
- Added fields: Location, Date & time, Name, Email, Phone, Additional Details, etc.
- Data stored securely via Formspree for later analysis by investigators.
- Usage of CloudFront & Route 53 services for secure deployment.
- Civilians: Upload images using mobile or camera footage.
- Law Enforcement: Cross-check faces with missing person records in real-time.
- HTML5, CSS3, Vanilla JS
- TailwindCSS for fast, responsive UI design
- AWS Lambda (Python) for processing logic
- Amazon Rekognition for facial matching
- Amazon API Gateway for REST API endpoints
- Amazon S3 for storing images and results
- CloudFront & Route 53 for secure deployment.
- Formspree for handling form submissions with added fields
- Image Upload: User uploads a photo and fills out the enhanced form.
- Face Matching: Rekognition compares the face with the missing persons dataset.
- Results: Matches (or no matches) returned instantly.
- Formspree: Metadata is stored for further analysis.
- Open website
- Manually search using filters
- Visually compare entries
- Login/Signup with validation & forgot password options
- Fill the enhanced form with contextual fields
- Upload a photo
- Instant result via Rekognition
- Formspree stores details for backend processing
- AWS Lambda tested with base64 image payloads and test events.
- Postman used to verify deployed API endpoints with JSON inputs.
- Video Demo available showing the updated upload form in action : https://youtu.be/kIx9YpGx90E .
- AWS Account with access to Lambda, Rekognition, API Gateway, S3, CloudFront & Route 53 .
- Formspree account for form data storage .
git clone https://github.com/Soumilgit/Real-Time-Missing-Persons-Detection.git-
Set up AWS services:
- Create an S3 bucket for storing images and feedback.
- Set up Lambda functions for face recognition.
- Use API Gateway to link the frontend to the backend.
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Deploy Frontend:
- Upload HTML, CSS, JS files to S3 or use Vercel/Netlify for deployment.
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Test the system:
- Upload an image, fill additional form details and make sure the face recognition provides accurate results.
This project is designed to grow:
- Serverless: Minimal infrastructure management with AWS Lambda.
- Modular Pages: Easy to add new features and pages as the project expands.
- S3 and API Gateway can handle a growing number of images in the database.
This project automates the search for missing persons using real-time face recognition. It provides a fast, accurate, and scalable solution that can be a game-changer in helping authorities identify missing people and reunite families.