https://github.com/mohammadshabazuddin/machine_learning_for_face_identification

Faces recognition project using Support Vector Machines (SVM) and Principal Component Analysis (PCA). It utilizes the Labeled Faces in the Wild (LFW) dataset, employs dimensionality reduction with PCA, and fine‑tunes SVM hyperparameters using RandomizedSearchCV.
https://github.com/mohammadshabazuddin/machine_learning_for_face_identification

eigenfaces principal-component-analysis randomizedsearchcv support-vector-machines

Last synced: about 1 year ago
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Faces recognition project using Support Vector Machines (SVM) and Principal Component Analysis (PCA). It utilizes the Labeled Faces in the Wild (LFW) dataset, employs dimensionality reduction with PCA, and fine‑tunes SVM hyperparameters using RandomizedSearchCV.

• Host: GitHub
• URL: https://github.com/mohammadshabazuddin/machine_learning_for_face_identification
• Owner: MohammadShabazuddin
• Created: 2023-04-30T22:04:03.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-05-30T18:59:38.000Z (about 2 years ago)
• Last Synced: 2025-04-10T23:54:15.418Z (about 1 year ago)
• Topics: eigenfaces, principal-component-analysis, randomizedsearchcv, support-vector-machines
• Language: Jupyter Notebook
• Homepage:
• Size: 169 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🌟 Facial Recognition Project 🌟

Welcome to our Facial Recognition Project! This project utilizes Support Vector Machines (SVM) and Principal Component Analysis (PCA) for accurate face identification. 🤖✨

## 📋 Project Overview

The facial recognition project is built with Python and employs the following key technologies:

- SVM for classification

- PCA for dimensionality reduction

- Labeled Faces in the Wild (LFW) dataset

- RandomizedSearchCV for hyperparameter tuning

## 🚀 Features

- SVM-based facial recognition

- PCA-based dimensionality reduction

- Fine-tuned SVM hyperparameters using RandomizedSearchCV

- Visualizations: Eigenfaces, Confusion Matrix, and Predicted vs. True Faces Gallery

## 🛠️ Setup and Usage

1. Install the required dependencies: `matplotlib`, `scikit-learn`

2. Run the Python script: `python facial_recognition_project.py`

## 📊 Results

- Achieved effective facial recognition on the LFW dataset

- Demonstrated the power of SVM and PCA in real-world applications

## 🌐 Explore the Code

Feel free to explore the code and customize it for your needs! Don't forget to star ⭐ the repo if you find it helpful.

## ✨ Contact

_Connect with me through various portals :_

  

    

    Social Media

    Username

    Link

  

  

    

    Email

    shabazuddin.198@gmail.com

    Email

  

  

    

    LinkedIn

    Shabazuddin Mohammad

    LinkedIn

  

  

    

    Instagram

    shabaz_uddin

    Instagram

  

  

    

    Facebook

    Shabaz

    Facebook

  

  

    

    Twitter

    shabazuddin786

    Twitter

  

I'm always open to collaboration and new opportunities! Feel free to reach out and connect with me. 🌟

Happy coding! 👩‍💻👨‍💻