@inproceedings{3aa2f7e45d994edea945601dd55e78b5,
title = "Dual-Stream Siamese Vision Transformer With Mutual Attention For Radar Gait Verification",
abstract = "The inconspicuousness of human gait characteristic in radar signal makes it hard to differentiate different identities. In this work, a Dual-stream Siamese Vision Transformer with Mutual Attention is proposed to verify whether a pair of radar gait sequences originate from the same person or not. The proposed Siamese Vision Transformer extracts pairwise discriminant spectral information from spectrograms and cadence velocity diagrams (CVDs). The proposed Mutual Attention scheme extracts the discriminant information from each stream through a self-attention mechanism and discovers the complement information cross the two streams through a cross-attention mechanism. The proposed method is evaluated on a large benchmark radar gait verification dataset. It significantly outperforms state-of-the-art solutions.",
keywords = "Cadence velocity diagram, Mutual attention, Radar gait verification, Siamese network, Spectrogram, Vision transformer",
author = "Ran Ji and Jiarui Li and Wentao He and Jianfeng Ren and Xudong Jiang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10095141",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "United States",
}