Software

Support Vector Machines in Face Recognition With Occlusions

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Executive Summary

Support Vector Machines (SVM) is one of the most useful techniques in classification problems. One clear example is face recognition. However, SVM cannot be applied when the feature vectors defining the authors' samples have missing entries. This is clearly the case in face recognition when occlusions are present in the training and/or testing sets. When k features are missing in a sample vector of class 1, these define an affine subspace of k dimensions.

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