The 3-D human pose estimation is a key task in the fields of computer vision and wireless sensing. Compared with optical sensors, through-wall (TW) radar can penetrate nonmetallic obstacles and capture reflected signals from targets, making it highly promising for applications in visually constrained environments. However, severe signal attenuation and low spatial resolution of TW radar make accurate human pose reconstruction still highly challenging. To this end, we propose an end-to-end multiperson 3-D pose estimation method based on multi-input multi-output (MIMO) TW radar and transformer (PERT). In PERT, we first extract fused multiscale features from horizontal and vertical radar heatmap sequences using a feature extractor. Subsequently, to enhance the focusing ability on effective regions in large-scale scenes and reduce computational overhead, we propose a signal-guided foreground selector (SFS) that leverages a signal-guided salience supervision mechanism to guide the selector in selecting tokens related to the targets. Finally, the spatiotemporal pose (STP) transformer module extracts fine-grained pose features from foreground tokens using an attention mechanism and predicts the positions of 3-D keypoints via a decoder. Experimental results demonstrate that PERT outperforms all baseline methods, achieving an average localization error of 6.20 cm in a large-scale $6\times 15$ m scene behind a 24-cm cement wall.
Suyun Sun, A. Kong, Jian Guo et al.· IEEE Transactions on Instrum...· 0 citations
Finger vein recognition (FVR) has significant potential in biometrics due to its high accuracy and intrinsic liveness detection capabilities. However, the increasingly stringent privacy regulations have presented severe data security challenges for traditional centralized training. While federated learning (FL) mitigates these privacy concerns through a decentralized training paradigm, conventional FL algorithms that seek a single global model experience significant performance degradation on non-independent and identically distributed (Non-IID) data in real-world cross-institutional deployments. This degradation stems primarily from a dual-heterogeneity issue that involves domain shift caused by hardware discrepancies across acquisition devices, and label skew resulting from nonoverlapping user identities. To address this dual-heterogeneity challenge, we propose a personalized federated learning framework driven by hierarchical parameter decoupling and subspace metric. First, we designed a hierarchical parameter decoupling architecture. Macroscopically, the architecture retains the classifier locally to isolate label heterogeneity; microscopically, it introduces an additive parameter decomposition that decouples the feature extractor on a global full-rank basis (to capture domain-invariant semantics, namely, the shared physiological vein topologies) and a local low-rank adapter (that accommodates device-specific characteristics, such as hardware-induced noise and illumination discrepancies). Furthermore, we propose a subspace similarity matching strategy based on principal angles on the Grassmann manifold. By exploiting the geometric properties of low-rank projection matrices, this strategy accurately quantifies the underlying distribution discrepancies among clients to guide personalized weighted aggregation. Extensive experiments on six public finger vein datasets demonstrate that the proposed framework significantly improves the overall recognition performance and mitigates performance degradation caused by data heterogeneity.
Ximing Zhou, Yuhan Wang, Jiajun Cui et al.· Italian National Conference...· 0 citations
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