Face Recognition Attendance System with FaceNet512 and RetinaFace

Traditional attendance methods often create friction. Card systems can be shared, fingerprints can fail in messy conditions, and manual processes are slow. A face recognition attendance system can reduce that friction when it is implemented carefully.

Main objective

The goal of this system was to allow employees to check in and out through a camera while keeping the process:

• fast
• reliable
• easy to integrate
• useful for reporting

Core stack

The solution combined:

• RetinaFace for face detection
• FaceNet512 for feature extraction and comparison
• FastAPI for service endpoints
• MariaDB for attendance and employee records

This stack worked well for a production-oriented system where model performance and backend integration both matter.

Real-world challenges

A face recognition system is not only about model accuracy in perfect conditions. Real usage introduces issues such as:

• inconsistent lighting
• different camera angles
• crowded environments
• repeated daily usage

Because of that, the detection pipeline needs to remain stable in normal operating conditions, not just in demos.

Backend responsibilities

Beyond recognition itself, the system also needed:

• employee registration
• face enrollment
• attendance logging
• reporting support
• integration with an existing backend flow

This is usually the point where an AI feature becomes a usable product component instead of a prototype.

Practical value

The main value is operational:

• less manual work
• faster check-in flow
• lower risk of attendance fraud
• easier reporting

In practice, the project was not only a computer vision demo. It was a real feature connected to business logic.