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FaceLog

FaceLog is a facial recognition logging system designed to automatically track student activity within laboratory environments.

The system continuously processes camera feeds to identify users, record their entry and exit times, and maintain a real-time activity log without requiring manual attendance tracking.

This project combines computer vision, machine learning, and distributed systems to build an automated monitoring platform.

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What This Documentation Covers​

This section describes the technical design and internal operation of FaceLog.

The documentation is divided into several parts:

  • Recognition Process — How the system detects and identifies faces.
  • Mathematical Model — The mathematical principles behind the recognition pipeline.
  • Architecture — The distributed system design and infrastructure.
  • Tech Stack — Technologies used to build the system.
  • Usage — Basic instructions for operating the system.

Each section focuses on a specific part of the system to make the documentation easier to explore.

System Goals​

FaceLog was designed with the following objectives:

  • Automate attendance logging using facial recognition
  • Monitor laboratory activity in real time
  • Identify both registered and unknown users
  • Maintain a persistent activity log
  • Provide administrators with tools for manual corrections

Core Technologies​

The system is built using a combination of technologies across several domains:

  • Computer Vision for face detection
  • Deep Learning for identity recognition
  • Distributed Infrastructure using containerized services
  • Web Applications for monitoring and administration

Explore the Documentation​

To understand how the system works, start with the following sections:

  • Recognition Process
  • Architecture
  • Mathematical Model
  • Tech Stack
  • System Usage

Together, these pages describe how FaceLog was designed and implemented.