Facenox

Abstract

1. Introduction

Facenox is an open-source, offline-first face recognition software for real-time attendance tracking. Built for privacy: No photos are saved. Biometric templates are end-to-end encrypted (E2EE).

2. Objective

Open-source, offline-first face recognition attendance platform that runs on any webcam.

This project demonstrates how TypeScript, FastAPI, Computer Vision can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

• On-Device AI Inference: Real-time face detection with recognition, ByteTrack multi-subject tracking, and liveness verification running entirely on local machine.
• Cross-Platform: Supports Windows, macOS, and Linux.
• Consent-Aware: Built-in compliance for biometric enrollment and deletion consent.
• Directory Management: Full control over group and member directories.
• Attendance Tracking: Automated session recording and CSV/JSON exports.
• Secure Storage: Encrypted local biometric storage and password-protected .facenox backups.
• Optional Sync: Secure pairing with Facenox Dashboard for centralized reporting. Encrypted face templates can sync between devices, using site-specific encryption keys.

4. Technology Stack

• No GPU Required: Real-time matching on standard CPUs.
• Environment: Optimized for controlled lighting and consistent setups.
• Hardware: Verified on hardware as old as 2nd-gen Intel i7 (2011), 4th-gen i3 (2015), and 8th-gen i5 (2018) laptops.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/facenox/facenox.git
cd facenox

# 1. Clone the repo
git clone https://github.com/facenox/facenox.git
cd facenox

# 2. Setup Local Backend
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate
pip install -r server/requirements.txt

# 3. Setup Desktop App
pnpm install

# 4. Run Development Workspace
./dev-start.sh

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by facenox and published on GitHub under the GNU Affero General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.