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arXiv · 2404.04829

Wi-Fi-based Personnel Identity Recognition: Addressing Dataset Imbalance with C-DDPMs

Jichen Bian·Chong Tan·Peiyao Tang·Min Zheng
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Abstract

Wireless sensing technologies become increasingly prevalent due to the ubiquitous nature of wireless signals and their inherent privacy-friendly characteristics. Device-free personnel identity recognition, a prevalent application in wireless sensing, is susceptibly challenged by imbalanced channel state information (CSI) datasets. This letter proposes a novel method for CSI dataset augmentation that employs Conditional Denoising Diffusion Probabilistic Models (C-DDPMs) to generate additional samples that address class imbalance issues. The augmentation markedly improves classification accuracies on our homemade dataset, elevating all classes to above 94%.

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Jichen Bian, Chong Tan, Peiyao Tang, Min Zheng. 2024-04-07. Wi-Fi-based Personnel Identity Recognition: Addressing Dataset Imbalance with C-DDPMs. https://doi.org/10.1109/lsp.2024.3397160

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