Computer Vision

Eigenfaces face recognition (MATLAB)


Eigenfaces is a well studied method of face recognition based on principal component analysis (PCA), popularised by the seminal work of Turk & Pentland. Although the approach has now largely been superseded, it is still often used as a benchmark to compare the performance of other algorithms against, and serves as a good introduction to subspace-based approaches to face recognition. In this post, I’ll provide a very simple implementation of eigenfaces face recognition using MATLAB.

PCA is a method of transforming a number of correlated variables into a smaller number of uncorrelated variables. Similar to how Fourier analysis is used to decompose a signal into a set of additive orthogonal sinusoids of varying frequencies, PCA decomposes a signal (or image) into a set of additive orthogonal basis vectors or eigenvectors. The main difference is that, while Fourier analysis uses a fixed set of basis functions, the PCA…

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