Inspiration
The inspiration for FindR Connect came from observing how missing-person cases often suffer from delayed identification despite the availability of cameras and digital infrastructure. The lack of a unified, technology-driven system motivated the idea of using AI to reduce response time and improve coordination.
What it does
FindR Connect is an AI-powered platform that helps locate missing persons by matching facial data with real-time camera feeds or uploaded images. When a high-confidence match is detected, the system generates alerts to support faster intervention.
How we built it
The project was built using computer vision and deep learning for facial recognition. Facial embeddings are generated and compared using similarity metrics, while a backend system manages case data, alerts, and time-based escalation.
Challenges we ran into
Key challenges included handling low-quality images, minimizing false positives, ensuring scalability, and addressing privacy and ethical considerations related to sensitive personal data.
Accomplishments that we're proud of
We successfully designed a working face-matching pipeline, implemented real-time alert logic, and structured the platform around responsible AI principles.
What we learned
This project improved our understanding of AI model deployment, system design, and the importance of ethical decision-making in real-world applications.
What's next for FindR Connect
Future plans include improving model accuracy, integrating additional data sources, enhancing security measures, and collaborating with authorized institutions for real-world validation.
Built With
- css
- firebase
- flask
- github
- google-cloud
- html5
- javascript
- numpy
- opencv
- python
- rest-api
- tensorflow
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