From Modalities to Styles: Rethinking the Domain Gap in Heterogeneous Face Recognition

arXiv:2404.14247v1 Announce Type: new
Abstract: Heterogeneous Face Recognition (HFR) focuses on matching faces from different domains, for instance, thermal to visible images, making Face Recognition (FR) systems more versatile for challenging scenarios. However, the domain gap between these domains and the limited large-scale datasets in the target HFR modalities make it challenging to develop robust HFR models from scratch. In our work, we view different modalities as distinct styles and propose a method to modulate feature maps of the target …

abstract arxiv cs.cv datasets domain domains face face recognition gap however images instance making recognition scale systems type