hectorkambow
commented
3 years ago @linetachieng
Seems like a helpful topic - lets please be sure it add value beyond what can be found in the official docs and other blog sites. Lets also make sure that it does not overlap with any existing EngEd articles or incoming topic suggestions (if you haven't already) to avoid any potential article closure. - approved
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Face recognition using PCA in Matlab
Introduction:
PCA is a way of reducing the dimensions of a large dataset by transforming the large dataset into a smaller dataset but ensuring that the smaller dataset contains more information of the large dataset. By reducing the dataset, we are increasing reducing the accuracy but PCA works on the principle of trading little accuracy fr simplicity. This is because smaller data are easier to explore and visualize making the analysis of data easier and faster for machine learning algorithms. Eigenvectors and eigenvalues are the linear algebra concept that is used to compute from the covariance matrix in order to determine the principal component of the data. Face recognition is the process of identifying an individual using their face. Matlab has numerous built-in functions that help calculate the principal components. In this article, we will see how we can use it to recognize the face.
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