TraceFusion: Precision AI for Missing and Wanted Person Detection
Abstract
With the advent of advanced technologies, which has paved the way for critical social challenges. One such social challenge is the identification of missing and wanted individuals in day-to-day basis. Manual identification is time consuming and less efficient to tackle this challenge.TraceFusion leverages Machine Learning(ML) to enhance efficiency in identification. The system features a Web based application that allows its users to register missing persons with image uploads, facilitating real time searches.The MTCNN model and the InceptionResNetV1 model process live camera feeds as well as recorded videos to match faces against a database of missing and wanted persons, which, upon detection, can assist the authorities in response actions. A flask based server performs real-time face recognition, while Firestore is used to store and retrieve images for matching. Using TraceFusion, public safety can be enhanced and investigations be fastened up.
Keywords:
Face Recognition, Missing Person Detection, Wanted Person Identification, Artificial Intelligence, MTCNN, Machine Learning, InceptionResNetV1, Firestore, Flask
Published
Issue
Section
License
Copyright (c) 2025 International Journal on Emerging Research Areas
This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Syam Gopi, Evelyn Susan Jacob, Joel John, Raynell Rajeev, Steve Alex, Survey on AI Malware Detection Methods and Cybersecurity Education , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Nikita Niteen , Juby Mathew, Securing AI: Understanding and Defending Against Adversarial Attacks in Deep Learning Systems , International Journal on Emerging Research Areas: Vol. 3 No. 2 (2023): IJERA
- Don Joseph, Fiyona Ann Sojan, Jimmy Mathew, Jobin Jomy Mathew, Bibin Varghese, A Review on Image and Video Processing with IoT-Enabled Supervised Learning for Intelligent Surveillance Systems , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Thejuskrishnan, Amal, Vyshnav M, Narayanan K, Saira Shamsudheen K S, SPEAK: An AI-Based Assistive Video Communication System for Speech and Sign Language Translation , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Amith Bino, Don Peter Joseph, Sreehari P, Anchal J Vattakunnel, Revolutionizing Nutritional Management Through Food Scanning And Object Detection: A New Android Application For Adults , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- M Manoj, A S Athira, Rishna Ramesh, Sandhra Gopi, Firoz P U, Smart Attend Insights , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Nighila Ashok, Adithya Ajith, Aparna Shaju, Arjuna Chandran V V, Fahmi Fathima T S, DeepScan : A Deepfake Video Detection System , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Sumi Joseph, Diana George, Ruthi Namburi, Dhanya Prathap, Artificial Intelligence in Opthamology:A study on different AIML approaches for Glaucoma prediction , International Journal on Emerging Research Areas: Vol. 6 No. 1 (2026): IJERA
- Aksa Ann Jacob, Midhun P Mathew, Adarsh S, Aaron Tom Viji, Aleena Varghese, A STUDY ON DISEASE DETECTION AND REMEDY IDENTIFICATION IN LEAVES , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Alan K George, Arpita Mary Mathew, Asin Mary Jacob, Elizabeth Antony, Shiney Thomas, Lung Cancer Subtype Classification Using Deep Learning Models , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
You may also start an advanced similarity search for this article.