Face Recognition Attendance & Authentication System

Project Overview

A real-time AI-powered system that uses Face Recognition and Computer Vision to identify users and provide secure authentication and automated attendance tracking.

The system captures a face through a camera, detects the face in real time, and uses a pretrained CNN model to extract unique facial features and generate a face embedding. The extracted features are then compared with stored facial data using Machine Learning and similarity matching to identify the user.

Once the person is successfully recognized, the system can authenticate their account and automatically record their check-in/check-out time. User information, facial embeddings, and attendance records are stored and managed through a backend system and database.

Key Features

Real-time Face Detection, Face Recognition & Identification, CNN-based Facial Feature Extraction, Face Embeddings, Automated Attendance Tracking, Face-based User Authentication, User Registration, Customer/User Data Management, Database Integration, RESTful API using FastAPI, and real-time processing and response.

Technology Stack

Python, FastAPI, Computer Vision, Deep Learning, Machine Learning, CNN, Face Recognition, REST API, Database, Git, and GitHub.