Behaviour biometrics are being explored as a viable alternative to overcome
the limitations of traditional authentication methods such as passwords and
static biometrics. Also, they are being considered as a viable authentication
method for IoT devices such as smart headsets with AR/VR capabilities,
wearables, and erables, that do not have a large form factor or the ability to
seamlessly interact with the user. Recent behavioural biometric solutions use
deep learning models that require large amounts of annotated training data.
Collecting such volumes of behaviour biometrics data raises privacy and
usability concerns. To this end, we propose using SimSiam-based non-contrastive
self-supervised learning to improve the label efficiency of behavioural
biometric systems. The key idea is to use large volumes of unlabelled (and
anonymised) data to build good feature extractors that can be subsequently used
in supervised settings. Using two EEG datasets, we show that at lower amounts
of labelled data, non-contrastive learning performs 4%-11% more than
conventional methods such as supervised learning and data augmentation. We also
show that, in general, self-supervised learning methods perform better than
other baselines. Finally, through careful experimentation, we show various
modifications that can be incorporated into the non-contrastive learning
process to archive high performance.

本文通过提出一种基于无对比度自监督学习的行为生物识别系统来提高标签效率，旨在解决行为生物识别系统所涉及的隐私和易用性问题，并通过实验证明了非对比学习表现优异，尤其在标记数据较少的情况下这种方法比监督学习和数据增强方法表现更佳。