This repo contains implementations of key unsupervised learning techniques, including image compression (K-means, GMM), PCA for Eigenfaces, ICA for audio separation, and CVAE for MNIST generation. It's a resource for understanding and applying foundational algorithms.

Applying Principal Component Analysis for image compression, exploring how varying numbers of principal components affect image quality and compression ratio.

Face Detection & Recognition System

Facial recognition via EigenFaces and FisherFaces methods

This repository contains the written work on the Bachelor thesis 'Identification of key information with topic analysis on large unstructured text data'.

Testing face recognition power of different algorithms on ORL database.

Python package that implements Eigenfaces to build a face recognition database. It supports interaction with the webcam.

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.

Face Recognition using method of Eigenfaces

Why should you care about Eigenvectors? A study on the efficacy of using eigenfaces in image classification.

Fourier decomposition of a chirp signal__Image preprocessing using Otsu thresholding__Edge detection using Canny ED__Automatically-aligned Palmprint__Face generation from Eigenfaces

Face Classification by SVM on Eigenfaces

Eigenfaces Program and Neural Network created from scratch that can recognize famous people through machine learning.

Face Recognition using PCA & LDA dimensionality reduction, then classification using KNN.

Multimedia Signal Processing

Application of PCA in facial recognition

This repository contains a collection of computer vision projects using OpenCV, DLib, and Mediapipe. It includes implementations of facial detection, facial classification, facial landmark detection, and hand tracking.

In this project, the focus is on image compression using Singular Value Decomposition (SVD), specifically following the principles of the well-known "Eigenfaces" experiment.

Real World Facial Image Classification by Dimensionality Reduction through PCA