[Programming] Principal Component Analysis

PCA is an approach used for face recognition and it is also enhanced to work for gender classification and skin detection.

Basic Principles of PCA

To decompose face images into a small set of characteristic feature images called Eigenfaces, which may be thought of as the principal components of the original images. These Eigenfaces function as the orthogonal basis vectors of a linear subspace called Face Space. Recognition is performed by projecting a new face image into this face space and then comparing it position in the face space with those of known faces.

Phases of PCA

A – Initialization
Acquisition of training set of face images and calculation of Eigenfaces.

B – RecognitionGiven an image to be recognized, calculate a set of weights of M Eigenfaces by projecting it onto each of the Eigenfaces. Determine if the face image is a face at all by checking if the image is sufficiently close to the face space. If it is a face, classify the weight pattern as either a known person or as unknown. If the same unknown face is seen several times, Eigenfaces and weight patterns are updated by calculating the new face’s characteristic weight and incorporating into the known faces.

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