Face Mask Recognition: Deep Learning based Desktop App

🎓 Face Mask Recognition: Deep Learning based Desktop App 🚀

Course Description

Welcome to the "Face Mask Recognition: Deep Learning based Desktop App" course! This comprehensive program is designed to guide you through the process of building your own face recognition system with a focus on mask detection. Using cutting-edge technologies like Python, TensorFlow 2, OpenCV, and PyQt, you'll develop a robust desktop application that can identify individuals even when they're wearing masks.

Project Overview 🖥️📱

What You'll Develop:

• A desktop application that recognizes faces through masks using deep learning.

Prerequisite for Project:

• Familiarity with OpenCV (we'll cover the basics in detail).

Course Breakdown 🛠️

Section -0: Setting Up Project

1. Install Python: Get started with Python, the programming powerhouse you'll use throughout this course.
2. Install Dependencies: Set up TensorFlow 2, OpenCV, and PyQt for your desktop application.

Section -1: Data Preprocessing 📊

1. Gather Images: Collect a dataset of images with faces, including those with masks.
2. Extract Faces: Isolate the face region from the collected images for precise recognition.
3. Labeling (Target output) Images: Annotate your images to guide the model in learning the correct patterns.
4. Data Preprocessing:
   • RGB mean subtraction: A crucial step to normalize your image data and improve model performance.

Section -2: Develop Deep Learning Model 🧠🤖

1. Training Face Recognition with OWN Deep Learning Model.
   • Dive into the world of Convolutional Neural Networks (CNNs), a key technique for image recognition tasks.
2. Model Evaluation: Test and refine your model to ensure it performs as expected.

Section -3: Prediction with CNN Model 🕵️‍♂️

• Combine all the components to build a model that can predict face identities in real-time.

Section -4: PyQT Basics 🗺️

• Learn the fundamentals of PyQt, the framework you'll use to create your desktop application's user interface.

Section -5: PyQt based Desktop Application 🖱️👤

1. Developing the Application: Turn your model into a functional desktop application that can process video streams in real-time.

Course Features ✨

• Hands-On Learning: Engage with practical, real-world projects that mirror actual industry scenarios.
• Step-by-Step Guidance: From setting up your environment to deploying your application, follow along with clear instructions and tips.
• Deep Dive into AI Concepts: Understand the mathematical foundations behind image processing and neural networks.
• Mini Project on Face Detection: A small yet enlightening project to solidify your understanding of face detection using OpenCV and deep learning.
• Comprehensive Coverage: Learn everything from image preprocessing to deploying a full-fledged application.
• Skill Development: Enhance your skills in Python, TensorFlow 2, OpenCV, and PyQt, all of which are crucial for AI and machine learning development.

Why Take This Course? 🚀💻

This course is not just about building a face mask recognition application; it's about equipping you with the skills to tackle complex computer vision problems using machine learning. Whether you're aiming to enter the field of AI, enhance your current skill set, or simply satisfy your curiosity for deep learning and its applications, this course has got you covered.

Join us on this exciting journey to create a desktop application that makes a difference in today's world, all from the comfort of your home. Sign up now and transform your knowledge into action! 🌟