shashank136 / face-detection-using-pca

Project to use Principal component analysis (PCA) for face detection

Unsupervised Learning

Project: Principal component analysis (PCA)

Install

This project requires Python 3.6 and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have software installed to run and execute an iPython Notebook

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run one of the following commands:

ipython notebook PCA Mini-Project.ipynb

or

jupyter notebook PCA Mini-Project.ipynb

This will open the iPython Notebook software and project file in your browser.

Dataset

The dataset used in this example is a preprocessed excerpt of the Labeled Faces in the Wild

Principal component analysis (PCA)

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. If there are n observations withp variables, then the number of distinct principal components is min ( n − 1 , p ).

PCA is many advantages when applied to ML algorihtms:

• Dimensionality reduction
• Feature transformation
• Data visualisation
• Speeds up the machine learning algorithms

How does PCA work?

PCA transforms the data into a new coordinate system such that the greatest variance by some projection of the data comes to lie on the first coordinate(called the principal component), the second greatest varaince on the second coordinate, and so on.

The idea is to increase the variance and decrease the total sum of distance of each points from the PC. In above figure the PC1 has maximum variance and minimum total distance sum compared to PC2.

Results for face recognition