Open-set semi-supervised audio-visual speaker recognition using co-training LDA and Sparse Representation Classifiers

Abstract

Semi-supervised learning is attracting growing interest within the biometrics community. Almost all prior work focuses on closed-set scenarios, in which samples labelled automatically are assumed to belong to an enrolled class. This is often not the case in realistic applications and thus open-set alternatives are needed. This paper proposes a new approach to open-set, semi-supervised learning based on co-training, Linear Discriminant Analysis (LDA) subspaces and Sparse Representation Classifiers (SRCs). Experiments on the standard MOBIO dataset show how the new approach can utilize automatically labelled data to augment a smaller, manually labelled dataset and thus improve the performance of an open-set audio-visual person recognition system.