Iterative Reference Driven Metric Learning for Signer Independent Isolated Sign Language Recognition

Abstract

Sign language recognition (SLR) is an interesting but difficult problem. One of the biggest challenges comes from the complex inter-signer variations. To address this problem, the basic idea in this paper is to learn a generic model which is robust to different signers. This generic model contains a group of sign references and a corresponding distance metric. The references are constructed by signer invariant representations of each sign class. Motivated by the fact that the probe samples should have high similarities with their own class references, we aim to learn a distance metric which pulls the samples and their true sign classes (references) closer and push away the samples from the false sign classes (references). Therefore, given a group of references, a distance metric can be exploited with our proposed Reference Driven Metric Learning (RDML). In a further step, to obtain more appropriate references, an iterative manner is conducted to update the references and distance metric alternately with iterative RDML (iRDML). The effectiveness and efficiency of the proposed method is evaluated extensively on several public databases for both SLR and human motion recognition tasks.