PCA method is less optimal in the separation between classes.ĭue to the useful features of faces this algorithm uses it's known as eigen faces. These important components are known as principle components. It takes all training faces of all people at once and looks at them as a whole an then it keeps the most important components and disards the rest. Įigenfaces algorithm works at the same principle. Eigenfaces algorithm works at the same principle. For example from nose to eyes there is a huge variation in everyone's face. When we look at a face we look at the places of maximum variation so that we can recognise that person. This algorithm follows the concept that all the parts of face are not equally important or useful for face recognition. In this article, we will focus on FisherFaces.Īs FisherFaces is an improvement over EigenFaces, we will go through some basic background on EigenFaces before going into FisherFaces so that the process is clear.
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