AI-Powered Web Platform for Locating Missing Individuals

Abstract

Every year, millions of individuals — kids, senior citizens, and victims of disasters or human trafficking — disappear from the world. Older methods rely largely on human intervention and tend to be sluggish and inefficient. An AI-driven system that addresses limitations on current traditional methods is proposed within the project. The model combines face recognition with a central repository were families, NGOs, common people, or the police can upload images of missing people. People can report found individuals through an online interface that cross-compares images uploaded with existing records in real time. The identification could be done by facial unique features analysis and match potential matches on system database. If it detects a match, it triggers notifications to Admin which verify and proceed the process. The alerts are sent to authorities and families via Twilio-based SMS/WhatsApp messages with GPS locations. This system connects technology and humanity — transforming data and AI into instruments of empathy and action to reunify families.

Introduction

The project “The Return Path” is an AI-powered web platform designed to speed up the search for missing persons by automating facial recognition and alert systems. It combines artificial intelligence, cloud computing, and geolocation to assist families, police, and the public in locating missing individuals more efficiently than traditional search methods.

System Overview

The platform operates through four main stages:

1. Data Collection and Uploads
   Families, police, or the public can upload information and photos of missing or found persons through a user-friendly web interface. The system also captures GPS coordinates using the JavaScript Geolocation API to record exact sighting locations.

2. Data Storage and Management
   All records and images are securely stored in a MongoDB cloud database. Every submission is first verified by an admin to ensure authenticity before being added to the Missing Person Database.

3. Face Encoding and Matching
   The system uses Face-api.js, a pre-trained AI facial recognition model, to generate a unique face descriptor (faceprint) for every uploaded image. When a new photo is submitted, it is compared with all stored faceprints. If a potential match is found, an alert is sent to the admin for human verification.

4. Real-Time Alert System
   Once an admin confirms a match, the system automatically notifies the concerned authorities or family members via Twilio API, sending real-time SMS or WhatsApp alerts.

Results and Discussion

Testing demonstrated that “The Return Path” is a fully functional and practical tool for real-world missing person searches.

• The homepage offers two clear options: “Report Missing Person” and “Report Found Person.”

• The admin dashboard handles verification, approval, and confirmation of potential matches.

• When a user reports a found person, the system automatically analyzes and compares the image in real time.

• If a match is confirmed, an instant notification is sent to the respective family or authorities.

This workflow ensures a balance between automation and human oversight, improving efficiency while maintaining security and accuracy.

Conclusion

In today’s world, where technology is growing so fast, we want to create something that could make a difference. Our AI generated platform to find missing people brings together power of Artificial Intelligence and web technologies to make the search for missing people faster, smarter and more reliable. By combining AI based facial recognition, image processing and geolocation capture, the system provides a structured and transparent way for the police, NGO’s and public. Each match lead is then verified by a human to maintain accuracy. In our initial testing phase, the facial recognition module performed consistently well. The web interface built with React and Node+.js were simple and intuitive for all types of users. The image upload and GPS capture worked smoothly. Looking ahead, we plan to integrate Twilio-based alert notifications and Google maps visualization to make responses even faster and more connected.’

References

[1] Ramavant Ganesh, Chilukuri Srija and Bhuriwale Devesh, “Finding a Missing Person Using AI,” International Research Journal on Advance Engineering Hub (IRJAEH), Vol. 3, Issue 1, 2025. [2] Hemadharshini S, Bheena Dhevi V and Bama Devi M, “AI Based – Assisted Seach for Missing People,” International Journal of Engineering Technology and Management Sciences, Vol. 7, Issue 2, 2023. [3] Pathipati Harshitha, Monali B Pipaliya and Gagan R, “Paper on Finding a Missing Person Using AI,” TIJER – International Research Journal, Vol. 10, Issue 12, 2023 [4] Sanskar Pawar, Lalit Bhadane, Amanullah Shaikh and Atharv Kumbhejkar, “Find Missing Person Using Artificial Intelligence,” International Research Journal of Engineering and Technology, Vol. 8, Issue 12, 2021. [5] Gorli Lakshmi Sai Amalodbhavi, Pindiga Urmila, Sanathan Reddy and Mohammed Mosim Ali, “Face Recognition and Tracking of Missing Person using AI,” Journal of Trends in Computer Science and Smart Technology, Vol. 7, Issue 1, 2025. [6] MD Hashir AJ, “An Intelligent AI-Driven System for Identifying Missing Persons,” TANZ, Vol. 20, Issue 5, 2025 [7] Ruchika Fegade, Pranali Nanaware and Shruti Kadu, “Finding Missing Person Using AI and ML”, International Journal of Novel Research and Development (IJNRD), Vol. 10, Issue 5, 2025. [8] Rupam Gautam, Vighnesh Bhandary, Mehveesh Shaikh and Simran Bhalla, “Missing Person Detection Using Facial Recognition,” Vol. 7, Issue 4, 2025.

Copyright

Copyright © 2025 Prachi Jain, Varun Kandunuri, Shrushti Pojage, Amit Pandey, Umang Pawar, Aman Gupta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.