A Facial Recognition System for Mason Korea Campus

This project aims to address the issue of reliable recognition and authentication of individuals, with a focus on representing diverse ethnicities and backgrounds. We build this prototype to address the 15% of students at the Mason Korea campus who cannot access their dorms, and was verified by 24 judges and participants.

Features:

• Reliable Recognition: The system can accurately detect and authenticate individuals.

• Diverse Representation: Trained on a diverse dataset to ensure recognition across different ethnicities.

• Real-time Detection: Implemented with an Arduino UNO LED Light that lights up when a face is detected.

Technologies Used:

Python: Core programming language for the project.

OpenCV: Used for image and video processing.

DeepFace: Library for deep learning-based face recognition.

NumPy: For numerical operations.

Arduino UNO: Microcontroller used for LED light integration.

Problem Statement: The project was developed to tackle the challenge faced by 15% of students at the Mason Korea campus who had issues with existing facial recognition systems that was used to predominantly Korean facial features. The goal was to create a system that could reliably recognize and authenticate individuals from diverse ethnic backgrounds.