https://github.com/soumilgit/real-time-missing-persons-detection
An MVP utilizing a custom API, where authorized users upload images to match individuals from a custom dataset of 50+ images.
https://github.com/soumilgit/real-time-missing-persons-detection
amazon-rekognition api-gateway aws-serverless cloudfront disaster-response emergency-response face-recognition image-matching missing-persons python-lambda route53 s3-storage
Last synced: 3 months ago
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An MVP utilizing a custom API, where authorized users upload images to match individuals from a custom dataset of 50+ images.
- Host: GitHub
- URL: https://github.com/soumilgit/real-time-missing-persons-detection
- Owner: Soumilgit
- License: mit
- Created: 2025-04-27T06:15:53.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-07-03T22:20:09.000Z (3 months ago)
- Last Synced: 2025-07-03T23:26:10.670Z (3 months ago)
- Topics: amazon-rekognition, api-gateway, aws-serverless, cloudfront, disaster-response, emergency-response, face-recognition, image-matching, missing-persons, python-lambda, route53, s3-storage
- Language: HTML
- Homepage: https://d1hn8ps4i2629d.cloudfront.net/
- Size: 184 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# Real-Time Missing Persons Detection
## AWS Architecture
## Overview
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.
---
## Key Features
- **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**.
---
## AWS Rekognition Terminal Output – Single Dataset Entry
---
## Problem
### Manual Process:
- Civilians browse websites and manually compare faces.
- Time-consuming, error-prone, and inefficient.
### Our Solution:
- 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.
---
## 🆕 What’s New?
### Upload Form Enhancements:
- 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.
---
## 💼 Real-World Use Cases
- **Civilians**: Upload images using mobile or camera footage.
- **Law Enforcement**: Cross-check faces with missing person records in real-time.
---
## 🛠️ Technologies Used
### Frontend:
- **HTML5**, **CSS3**, **Vanilla JS**
- **TailwindCSS** for fast, responsive UI design
### Backend:
- **AWS Lambda (Python)** for processing logic
- **Amazon Rekognition** for facial matching
- **Amazon API Gateway** for custom REST API endpoints
- **Amazon S3** for storing images and results
- **CloudFront** & **Route 53** for secure deployment.
- **Formspree** for handling form submissions with added fields
---
## How It Works
1. **Image Upload**: User uploads a photo and fills out the enhanced form.
2. **Face Matching**: Rekognition compares the face with the missing persons dataset.
3. **Results**: Matches (or no matches) returned instantly.
4. **Formspree**: Metadata is stored for further analysis.
---
## Project Flow
### Old Method:
- Open website
- Manually search using filters
- Visually compare entries
### New Method:
1. **Login/Signup** with validation & forgot password options
2. **Fill the enhanced form** with contextual fields
3. **Upload a photo**
4. **Instant result** via Rekognition
5. **Formspree stores details** for backend processing
---
## Testing & Validation
- **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 .
---
## Installation & Setup
### Prerequisites:
- AWS Account with access to **Lambda**, **Rekognition**, **API Gateway**, **S3**, **CloudFront** & **Route 53** .
- **Formspree** account for form data storage .
### Setup Steps:
```bash
git clone https://github.com/Soumilgit/Real-Time-Missing-Persons-Detection.git
```
2. **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.
3. **Deploy Frontend**:
- Upload HTML, CSS, JS files to S3 or use Vercel/Netlify for deployment.
4. **Test the system**:
- Upload an image, fill additional form details and make sure the face recognition provides accurate results.
## Scalability
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.
## Conclusion
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.
## License
This project is licensed under the  .