FingerCheck 2.0

In brief

FingerCheck 2.0 is a Directrix Technologies project for University of East London, UK: Biometric Recognition & Attendance. Summary: Fingerprint recognition using Siamese neural networks, integrated into an attendance application. Outcome: Built fingerprint recognition with machine learning into a working attendance application, with tools for inspecting model behavior.

Overview

FingerCheck 2.0 is a biometric attendance system that uses machine learning for fingerprint recognition.

The work involved integrating biometric recognition models with application services and providing interfaces for inspecting fingerprint features and model behavior.

What we built

• A fingerprint recognition component using Siamese neural networks.
• REST API integration for machine learning models.
• Backend services for application integration.
• A web application interface.
• Interactive fingerprint feature visualization.
• Containerized training workflows using GPU-enabled infrastructure.

How we built it

We used a Siamese-network approach to learn representations suitable for comparing fingerprint inputs. REST APIs connect the model to the web application.

A web interface supports the attendance workflow, and an interactive visual tool lets the team explore model features.

Containerized training workflows and cloud GPU resources supported machine learning experimentation and integration.

Services this project shows

• Computer vision and image analysis
• Predictive analytics and applied ML
• Custom web application development

Project outcome

Built fingerprint recognition with machine learning into a working attendance application, with tools for inspecting model behavior.

Potential applications

• Attendance management.
• Biometric matching.
• Identity verification prototypes.
• Secure access control.