Bridging face and sound modalities through Domain Adaptation Metric Learning

Robust emotion recognition systems require extensive training by employing huge number of training samples with purpose of generating sophisticated models. Furthermore, research is mostly focused on facial expression recognition due, mainly to, the wide availability of related datasets. However, the existence of rich and publicly available datasets is not the case for other modalities like sound and so forth. In this work, a heterogeneous domain adaptation framework is introduced for bridging two inherently different domains (namely face and audio). The purpose is to perform affect recognition on the modality where only a small amount of data is available, leveraging large amounts of data from another modality.