Survey of Distance Measures for NMF-Based Face Recognition

Abstract

Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that can extract parts from visual data. The goal of this technique is to find intuitive basis such that training examples can be faithfully reconstructed using linear combination of basis images which are restricted to non-negative values. Thus NMF basis images can be understood as localized features that correspond better with intuitive notions of parts of images. However, there has not been any systematic study to identify suitable distance measure for using NMF basis images for face recognition. In this article we evaluate the performance of 17 distance measures between feature vectors based on the result of the NMF algorithm for face recognition. Recognition experiments are performed using the MIT-CBCL database, CMU AMP Face Expression database and YaleB database.