/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.

Primary LanguageJupyter Notebook

🌟 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! 👩‍💻👨‍💻