Maximum Margin Discriminant Analysis based Face Recognition

Face recognition is a highly non-trivial classification problem since the input is high-dimensional and there are many classes with just a few examples per class. In this paper we propose using a recent algorithm – Maximum Margin Discriminant Analysis (MMDA) – to solve face recognition problems. MMDA is a feature extraction method that is derived from a set of sound principles: (i) each feature should maximize information transmission about the classification labels, (ii) only the decision boundary should determine the features and (iii) features should reveal independent information about the class labels. Previously, MMDA was shown to yield good performance scores on a number of standard benchmark problems. Here we show that MMDA is capable of finding good features in face recognition and performs very well provided it is preceded by an appropriate preprocessing phase.