Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/yonatankinfe/mathlabfacedetector
Welcome to the Custom Face Detector project! This repository offers code and resources for creating a custom face detection system using MATLAB. Utilizing Convolutional Neural Networks (CNNs), the project aims to accurately detect and classify faces in images. Whether you're a beginner or an advanced user, this project provides a practical example
https://github.com/yonatankinfe/mathlabfacedetector
Last synced: about 2 months ago
JSON representation
Welcome to the Custom Face Detector project! This repository offers code and resources for creating a custom face detection system using MATLAB. Utilizing Convolutional Neural Networks (CNNs), the project aims to accurately detect and classify faces in images. Whether you're a beginner or an advanced user, this project provides a practical example
- Host: GitHub
- URL: https://github.com/yonatankinfe/mathlabfacedetector
- Owner: Yonatankinfe
- Created: 2024-06-27T14:03:13.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-06-27T19:17:20.000Z (6 months ago)
- Last Synced: 2024-06-27T22:14:41.944Z (6 months ago)
- Language: MATLAB
- Homepage:
- Size: 1.48 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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 📷🖼️
