Face Recognizer: Real-Time Face Detection and Verification with MTCNN, FaceNet, and SVM for Secure Access Control

/ Computer Vision & Image Processing, Generative AI on AWS use cases / By

Description

The Face Recognizer uses MTCNN for accurate face detection and FaceNet for generating face embeddings, which are classified by an SVM Classifier. Developed with Python and PyQT5, it offers real-time face recognition through an intuitive desktop application for secure access control and identity verification.

Practical Use Case and User Story

As a security officer, I need a system that detects faces in images or video feeds using MTCNN, extracts features with FaceNet, and classifies identities with an SVM classifier. Users should be able to interact through a PyQT5 desktop application to upload new faces, train the model, and run recognition tests. This will enhance security by accurately identifying known individuals and managing new entries efficiently.

Tech Stack Involved

MTCNN (Multi-task Cascaded Convolutional Networks) FaceNet, SVM Classifier, PyQT5, OpenCV, Numpy and Scikit-learn

Demo

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