Touch-stroke dynamics have garnered significant attention as a means of mobile user authentication. However, current research often assumes the availability of large datasets tailored to specific smartphone applications. In response to the scarcity of real-world data and the computational limitations of smartphones, we introduce an innovative cross-domain cross-task transfer framework for mobile user authentication. Our approach harnesses auxiliary data from diverse applications and user sets to complement the data scarcity. A two-stream Transformer network is devised to enable knowledge transfer between disparate domains and tasks. This network adeptly amalgamates user-discriminative insights from various applications while refining precise embeddings tailored to a target application. Meta-learning optimization is employed to counteract negative knowledge sharing. Furthermore, we introduce a novel data augmentation technique based on feature covariance to bolster the training of robust one-class classifiers. Experimental findings showcased the efficacy of our method in harnessing heterogeneous data sources that exhibit domain and task disparities. Our approach achieved commendable performance to state-of-the-art methods with a reasonable computational burden on smartphones.

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.