Millimeter-wave Radar Point Cloud Gait Recognition Method Under Open-set Conditions Based on Similarity Prediction and Automatic Threshold Estimation

DU Lan; LI Yiming; XUE Shikun; SHI Yu; CHEN Jian; LI Zhenfang

doi:10.11999/JEIT241034

Volume 47 Issue 6

Jun. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(6): 1850-1863

DU Lan, LI Yiming, XUE Shikun, SHI Yu, CHEN Jian, LI Zhenfang. Millimeter-wave Radar Point Cloud Gait Recognition Method Under Open-set Conditions Based on Similarity Prediction and Automatic Threshold Estimation[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1850-1863. doi: 10.11999/JEIT241034

Citation:

DU Lan, LI Yiming, XUE Shikun, SHI Yu, CHEN Jian, LI Zhenfang. Millimeter-wave Radar Point Cloud Gait Recognition Method Under Open-set Conditions Based on Similarity Prediction and Automatic Threshold Estimation[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1850-1863. doi: 10.11999/JEIT241034

Citation:

DU Lan, LI Yiming, XUE Shikun, SHI Yu, CHEN Jian, LI Zhenfang. Millimeter-wave Radar Point Cloud Gait Recognition Method Under Open-set Conditions Based on Similarity Prediction and Automatic Threshold Estimation[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1850-1863. doi: 10.11999/JEIT241034

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Millimeter-wave Radar Point Cloud Gait Recognition Method Under Open-set Conditions Based on Similarity Prediction and Automatic Threshold Estimation

doi: 10.11999/JEIT241034 cstr: 32379.14.JEIT241034

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (U21B2039, U24B20137, 62201433), The Fundamental Research Funds for the Central Universities (QTZX23067)

Received Date: 2024-11-20
Rev Recd Date: 2025-04-30

Available Online: 2025-05-16

Publish Date: 2025-06-30

Abstract

Abstract

Objective Radar-based gait recognition systems are typically developed under closed-set assumptions, limiting their applicability in real-world scenarios where unknown individuals frequently occur. This constraint presents challenges in security-critical settings such as surveillance and access control, where both accurate recognition of known individuals and reliable exclusion of unknown identities are essential. Existing methods often lack effective mechanisms to differentiate between known and unknown classes, leading to elevated false acceptance rates and security risks. To overcome this limitation, this study proposes an open-set recognition framework that integrates a similarity prediction network with an adaptive thresholding method based on Extreme Value Theory (EVT). The framework models the score distributions of known and unknown classes to enable robust identification of unfamiliar identities without requiring samples from unknown classes during training. The proposed method enhances the robustness and applicability of millimeter-wave radar-based gait recognition under open-set conditions, supporting its deployment in operational environments. Methods The proposed method comprises four key modules: point cloud feature extraction network training, similarity prediction network training, automatic threshold estimation, and open-set testing. A sequential training strategy is adopted to ensure robust learning. First, the point cloud feature extraction network is trained with a triplet loss function that encourages intra-class compactness and inter-class separability by pulling same-class samples closer and pushing different-class samples apart. This enables the network to learn stable and discriminative representations, even under variations in viewpoint or clothing. The extracted features are then input into a similarity prediction network trained to model the score distributions of known and unknown identities. By incorporating score-based constraints, the network learns a decision space in which known and unknown classes are more effectively separated. Following network optimization, an EVT-based thresholding module is employed. This module dynamically models the tail distributions of similarity scores and automatically determines a class-agnostic threshold by minimizing the joint false acceptance and false rejection rates. This adaptive and theoretically grounded strategy enhances the separation between known and unknown classes in the similarity space. Together, these modules improve the stability and accuracy of radar-based gait recognition under open-set conditions, supporting reliable operation in real-world scenarios where unfamiliar individuals may appear. Results and Discussions The proposed method improves distributional separation between known and unknown classes in the similarity score space through the similarity prediction network and distinguishes them effectively using adaptive thresholding. Experimental results show that the method consistently yields higher F1 scores across all openness levels compared with baseline approaches, indicating strong robustness to open-set variations (Table 1). Specifically, the method achieves an 87% recognition rate for known classes and a 96% rejection rate for unknown classes, outperforming all comparison methods (Fig. 7). Ablation experiments confirm that incorporating the similarity prediction module enhances recognition performance under high openness. Manually set thresholds, while effective under low openness, show substantial performance degradation under large openness (F1 score: 43.93%). In contrast, the proposed automatic thresholding module demonstrates superior generalization, improving the F1 score by 22.88% under large openness conditions (Table 2). Further analysis shows that the method significantly increases the score distribution gap between known and unknown classes, contributing to improved recognition reliability (Fig. 8). Comparative evaluations (Table 3) confirm that the method achieves superior open-set recognition performance. In addition, the employed point cloud feature extraction network captures temporal features at multiple time scales and uses an attention-based mechanism to adaptively aggregate information across frames and temporal resolutions. This contributes to more robust gait representations and further improves open-set recognition performance compared with other feature extraction networks (Table 4). Conclusions Building on previous work on robust feature extraction under complex covariate conditions, this study extends millimeter-wave radar point cloud gait recognition to open-set scenarios. The proposed method preserves the recognition strength of the original feature extraction network and enhances class discriminability by integrating a similarity prediction network. To address the limitations of manually defined rejection thresholds, an automatic threshold determination module based on EVT is introduced. Extensive experiments using measured millimeter-wave radar point cloud gait data confirm that the method reliably distinguishes between known and unknown individuals, demonstrating its effectiveness and robustness under open-set conditions.
- Millimeter-wave radar,
- Gait recognition,
- Open-set recognition,
- Similarity prediction,
- Extreme Value Theory (EVT)

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References(22)

References

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