Support Vector Machines in Face Recognition with Occlusions

Support Vector Machines (SVM) are 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 our 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. The goal of the SVM is to maximize the margin between the vectors of class 1 and class 2 on those dimensions with no missing elements and, at the same time, maximize the margin between the vectors in class 2 and the affine subspace of class 1. This second term of the SVM criterion will minimize the overlap between the classification hyperplane and the subspace of solutions in class 1, because we do not know which values in this subspace a test vector can take. The hyperplane minimizing this overlap is obviously the one paralle...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Aleix M. Martínez, Hongjun Jia
• Keywords: Computer Vision, CVPR 2009, Face Recognition, Support Vector Machines