Custom Face Detector Using AI in MATLAB 🤖📷

Introduction

Welcome to the Custom Face Detector project! 🎉 This repository contains code and resources for building a custom face detection system using MATLAB. 🧠💻 By leveraging Convolutional Neural Networks (CNNs), this project aims to accurately detect and classify faces from a set of images. 👥📸 Whether you are a beginner or an advanced user, this project provides a practical example of how to implement machine learning for image processing tasks. 📊🔍

Project Overview

This project demonstrates how to create a custom face detector using a Convolutional Neural Network (CNN) in MATLAB. 🚀 The steps include preparing the dataset, training the model, detecting faces in new images, and evaluating the model's performance. 💡🔬 Here’s a brief overview of what the code does: 📝

Data Preparation 🗂️

• Load images of two individuals (Yonatan and another person) from specified folders. 🧑📁

• Combine the images into a single datastore. 🖼️➡️📚

• Split the data into training and validation sets. 🏋️‍♂️📝

Data Augmentation 🔄📈

• Resize the images to match the input size expected by the CNN. 📏🖼️

• Convert grayscale images to RGB format. 🌑➡️🌈

CNN Architecture 🧠🏗️

Define a simple CNN with several convolutional, batch normalization, ReLU, and max-pooling layers. 🌀📊

Add fully connected, softmax, and classification layers for the final output. 🔗🧮✅

Training 🎯

• Set training options such as the optimizer, learning rate, and number of epochs. ⚙️⏳

• Train the CNN using the augmented training data. 🏋️‍♀️🖼️

• Save the trained model. 💾📊

Face Detection 👥🔍

• Use a pre-trained model to detect faces in new images. 🤖📷

• Crop and resize the detected faces. ✂️📏

• Classify the faces using the trained CNN. 🧠👥

Evaluation 🧪📊

• Load test data and make predictions using the trained model. 📂🔮

• Display a confusion matrix to visualize the performance. 🔄📊

• Calculate and display the accuracy of the model. 📈🎯

This project provides a comprehensive example of using MATLAB for image classification and face detection tasks, leveraging the power of convolutional neural networks and data augmentation techniques. 🚀📸

Here are some images 📷🖼️