On the Representation Learning of Conditional Biometrics for Flexible Deployment

Tiong-Sik Ng, Cheng-Yaw Low, Jacky Chen Long Chai, Andrew Beng Jin Teoh

Abstract: Unimodal biometric systems are commonplace nowadays. However, there remains room for performance improvement. Multimodal biometrics, i.e., the combination of more than one biometric modality, is one of the promising remedies; yet, there lie various limitations in deployment, e.g., availability, template management, deployment cost, etc. In this paper, we propose a new notion dubbed Conditional Biometrics representation for flexible biometrics deployment, whereby a biometric modality is utilized to condition another for representation learning. We demonstrate the proposed conditioned representation learning on the face and periocular biometrics via a deep network dubbed the Conditional Biometrics Network. Our proposed Conditional Biometrics Network is a representation extractor for unimodal, multimodal, and cross-modal matching during deployment. Our experimental results on five in-the-wild periocular-face datasets demonstrate that the network outperforms their respective baselines for identification and verification tasks in all deployment scenarios.

External IDs:dblp:journals/access/NgLCT23