Pretrained Implicit-Ensemble Transformer for Open-Set Authentication on Multimodal Mobile Biometrics

Smartphones have become indispensable in our lives, even for security-critical tasks. Traditional security measures such as PINs provide only one-time authentication, while biometrics enable continuous authentication in mobile devices. This paper introduces a simple, lightweight, pretrained Transformer dubbed PIEformer for open-set authentication (OSA) of multimodal touchstrokes and gait biometrics. Compared to conventional mobile closed-set authentication, OSA enables more secure and practical authentication, with genuine and impostor users disjoint from the training set. PIEFormer incorporates a novel implicit ensembling mechanism for extracting discriminative embeddings within an open-set environment and enhancing generalization performance. This approach learns multiple diverse sub-embeddings, capturing complementary aspects of biometrics data with minimal computational overhead, allowing Transformers to exhibit robust capabilities in OSA. Our proposed methods demonstrate state-of-the-art results on HMOG and BBMAS datasets, particularly in open-set scenarios compared to closed-set literature, thus bringing mobile biometric authentication closer to real-world applications.