Through-Wall Cross-Domain User Identification via Lip Movement Micro-Doppler and MIMO Radar: an Unsupervised Domain Adaptation Approach

Lip movement-based user identification holds significant promise for public security and intelligent surveillance due to its dynamic patterns, forgery resistance, and individual distinctiveness. Recently, millimeter-wave radar has been employed for contactless identification, offering advantages such as light insensitivity, privacy preservation, and sensitivity to fine motion. However, its limited wall penetration and vulnerability to occlusion present ongoing challenges. Moreover, existing recognition approaches rely heavily on supervised learning, demanding large labeled datasets and exhibiting poor generalization across domains. To overcome these limitations, we propose Lip-TWCDID, a lip movement-based cross-domain user identification system using 1–2 GHz MIMO radar. The use of low-frequency signals enhances penetration, while the MIMO architecture improves spatial resolution, enabling stable detection of fine-grained micro-Doppler signatures of lip movements through a 22 cm brick wall. To reduce dependence on labeled data and improve domain generalization, we introduce a novel unsupervised domain adaptation (UDA) framework, consistency-adversarial-contrastive learning (CACL), which integrates pseudo-label consistency learning, domain adversarial training, and pseudo-supervised contrastive learning. Specifically, pseudo-label consistency enforces prediction consistency under input perturbations, improving robustness; domain adversarial training introduces a domain discriminator to encourage domaininvariant feature learning and align feature distributions; pseudosupervised contrastive learning leverages high-confidence pseudolabels to perform contrastive learning in the feature space, enhancing inter-class separability and intra-class compactness. By jointly optimizing these components, CACL effectively adapts to unlabeled target domains while minimizing annotation costs. Extensive experiments demonstrate that CACL outperforms state-of-the-art UDA methods and significantly improves the generalization and robustness of through-wall user identification.