A taxonomy of emerging multilinear discriminant analysis solutions for biometric signal recognition

Abstract

Biometric signals are mostly multidimensional objects, known as tensors. Recently, there has been a growing interest in multilinear discriminant analysis (MLDA) solutions operating directly on these tensorial data. However, the relationships among these algorithms and their connections to linear (vector-based) algorithms are not clear, and in-depth understanding is needed for further developments and applications. In this chapter, we introduce the basics needed in understanding existing MLDA solutions and then categorize them according to the multilinear projection employed, while pointing out their connections with traditional linear solutions at the same time. A number of commonly used objective criteria and initialization methods are discussed. Experiments are carried out on two public face databases to evaluate the performance of the MLDA variants, and the results show that MLDA (and multilinear learning algorithms in general) is a promising field with great research potential.