Periocular Biometrics Enhancement Through Multimodal Embeddings And Classifier Adaptation

Abstract

Conditional Multimodal Biometrics (CMB) presents a promising avenue for boosting periocular biometrics performance by conditioning facial information. This paper reframes CMB as a domain adaptation problem arising from distinct distribution gaps between facial and periocular modalities. Despite shared identity labels, classifiers across these domains suffer from misalignment. To address this problem, we propose a novel approach that strategically employs adaptation techniques in embeddings and classifiers. Our novel strategy combines supervised contrastive embedding adaptation to bridge modality gaps and introduces modality transfer augmentation to enrich facial embeddings with periocular semantic cues. This augmentation procedure guides classifier adaptation towards the periocular domain. Empirical validation conducted across six diverse periocular-face datasets underscores the efficacy of the proposed method.