What we are building
Face Lab is a full-stack facial recognition demo. The frontend is Astro, the backend is FastAPI,
and the recognition layer uses dlib through the face_recognition Python library.
School capstone, computer science
Face recognition, shown step by step.
This project demonstrates how a camera image becomes a face embedding, how the backend compares it with saved samples, and why a match is accepted or rejected.
- 01Detect face
- 02Encode features
- 03Compare distance
- 04Return confidence
Why it matters
The goal is not a mystery button. The interface shows detection count, distance, confidence, and failure states so the model's decision can be explained during a school presentation.
Workflow
From camera frame to answer
- Register a person with clear camera captures or uploaded images.
- The API finds the most useful face in the image and stores its 128-value embedding.
- Identify compares a new image against saved embeddings and returns the closest known person.
- Verify compares two images directly and reports whether they appear to be the same person.