Face-Recognition

This project implements a face recognition pipeline using the AT&T Face Dataset (ORL Dataset). It includes dimensionality reduction techniques like PCA, clustering algorithms such as K-Means and GMM, and an optional Autoencoder-based feature extraction.

Features

• Dataset Handling: Downloads and processes the AT&T Face Dataset.
• Dimensionality Reduction: Implements PCA for feature extraction and dimensionality reduction.
• Clustering:
  • K-Means Clustering
  • Gaussian Mixture Model (GMM)
• Autoencoders: Optional feature extraction using Autoencoders.
• Evaluation:
  • Clustering accuracy using the Hungarian algorithm.
  • F1 Score and Confusion Matrix visualization.