CATFSID: A few-shot human identification system based on cross-domain adversarial training

Abstract

With the advancement of wireless sensing technology, human identification based on WiFi sensing has garnered significant attention in the fields of human–computer interaction and home security. Despite the initial success of WiFi sensing based human identification when the environment is fixed, the performance of the trained identity sensing model will be severely degraded when applied to unfamiliar environments. In this paper, a cross-domain human identification system (CATFSID) is proposed, which is able to achieve environment migration of trained model using up to 3-shot. CATFSID utilizes a dual adversarial training network, including cross-adversarial training between source and source domain classifiers, and adversarial training between source and target domain discriminators to extract environment-independent identity features. Introducing a method based on pseudo-label prediction, which assigns labels to target domain samples similar to the source domain samples, reduces the distribution bias of identity features between the source and target domains. The experimental results show accuracy of 90.1% and F1-Score of 89.33% when using 3 samples per user in the new environment.