Physics-Aware Reconstruction for Millimeter-Wave Radar Gait Recognition Under Complex Wearing Scenarios

HUANG Ling; QIU Liying; WANG Jiacheng; HAN Penglin; ZHOU Qingdi; YAN Huimei

doi:10.11999/JEIT260522

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HUANG Ling, QIU Liying, WANG Jiacheng, HAN Penglin, ZHOU Qingdi, YAN Huimei. Physics-Aware Reconstruction for Millimeter-Wave Radar Gait Recognition Under Complex Wearing Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260522

Citation:

HUANG Ling, QIU Liying, WANG Jiacheng, HAN Penglin, ZHOU Qingdi, YAN Huimei. Physics-Aware Reconstruction for Millimeter-Wave Radar Gait Recognition Under Complex Wearing Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260522

Citation:

HUANG Ling, QIU Liying, WANG Jiacheng, HAN Penglin, ZHOU Qingdi, YAN Huimei. Physics-Aware Reconstruction for Millimeter-Wave Radar Gait Recognition Under Complex Wearing Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260522

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Physics-Aware Reconstruction for Millimeter-Wave Radar Gait Recognition Under Complex Wearing Scenarios

doi: 10.11999/JEIT260522 cstr: 32379.14.JEIT260522

school of Modern Microelectronics Industry, Lanzhou University of Technology, Lanzhou 730050, China

Funds: Gansu Provincial Education Department Industrial Support Program (2026CYZC-027), The Key Project of Natural Science Foundation of Gansu Province (25JRRA062), 2025CYZC-02), Gansu Provincial Key Talent Program (2025RCXM022)

Received Date: 2026-04-27
Accepted Date: 2026-07-14
Rev Recd Date: 2026-07-14

Available Online: 2026-07-24

Abstract

Abstract

Objective Millimeter-wave (MMW) radar gait recognition has demonstrated considerable potential in the domain of non-contact biometric identification, primarily due to its inherent advantages in privacy preservation and resilience to variable lighting conditions. However, a significant challenge persists when applying these systems to unconstrained real-world environments where subjects may wear complex clothing, such as long coats or carry backpacks. These external covariates introduce non-stationary, high-frequency spectral components that result in severe spectral aliasing and masking of the intrinsic micro-Doppler (m-D) signatures of the human body. Traditional deep learning approaches usually treat radar spectrograms as generic image data and neglect the physical relationship between Doppler frequency shifts and human motion. Consequently, clothing-induced interference may overlap with motion-related signals, reducing recognition accuracy. Therefore, it is imperative to develop a framework that integrates radar physics and biomechanical properties to achieve effective signal decoupling and interference suppression. This study aims to provide a robust solution for radar-based gait recognition in complex scenarios by incorporating explicit physical constraints into the neural network architecture. Methods To mitigate the adverse effects of clothing-induced interference, this paper introduces PRISM-Net, a physics-aware reconstruction framework for millimeter-wave radar gait recognition. The framework is grounded in the biomechanical characteristics of human motion, specifically the observation that the human torso, representing the primary mass, generates stable and low-frequency Doppler shifts, whereas the movement of limbs produces higher-frequency, periodically alternating spectral components. (1) Physics-aware Frequency Structural Reconstruction: The proposed method diverges from conventional uniform processing by implementing a structural decoupling strategy. Based on the velocity distribution defined by biomechanical properties, the aliased original m-D spectrogram is partitioned into discrete frequency bands. The low-frequency band is dedicated to the torso, capturing stable and identity-persistent features, while the high-frequency band encompasses limb dynamics. This spatial-frequency partitioning enables the network to isolate the spectral regions most susceptible to clothing-induced clutter at the physical layer, thereby suppressing interference propagation. (2) Adaptive Weighted Attention Mechanism (WAM): An adaptive WAM module is integrated to regulate the signal-to-noise ratio (SNR) in the feature space. Given that clothing interference is dynamic and predominantly occupies high-frequency regions, the WAM adaptively evaluates the reliability of different frequency-derived features. In cases where the high-frequency limb features are corrupted by non-stationary noise from swinging garments, the WAM applies soft-thresholding to reduce their response weights. Simultaneously, the contribution of the more reliable torso-related features is enhanced. (3) Experimental Configuration and Protocol: The model is evaluated using the MMRGait-1.0 dataset, which provides a comprehensive set of radar gait signatures under various covariates. To ensure the assessment reflects real-world generalization, an open-set testing protocol is strictly followed. The training set comprises data from 74 subjects, while 47 entirely unseen subjects are used for evaluation. All radar spectrograms are processed to a standard resolution of 224 × 224 pixels. The network is optimized using the AdamW algorithm with a joint loss function consisting of cross-entropy and feature regularization components to ensure both classification accuracy and feature discriminability. Results and Discussions The experimental results demonstrate the effectiveness of integrating the biomechanical characteristics of human motion into the recognition process. As shown in Table 1, PRISM-Net achieves an average Rank-1 recognition rate of 89.7% in the 90-degree side-view scenario. In the coat (CT) occlusion scenario, the model maintains an accuracy of 85.1%. This is a 18.1% improvement over traditional lightweight models such as ShuffleNetV2 and a 7.4% improvement over the standard ResNet-18 baseline. The stability of the model was verified through ten independent repeated trials. As illustrated in the box plot in Fig. 1, PRISM-Net shows a low standard deviation of ±0.55%, whereas the baseline model exhibits a higher deviation of ±1.75%. An independent samples t-test results in a p-value of less than 0.001, confirming statistical significance. Ablation studies in Table 2 confirm the contribution of each module. Removing frequency decoupling reduces accuracy in the CT scenario to 75.5%, proving that physical structural separation is necessary to prevent feature distortion. The WAM module further provides a 4.2% accuracy gain by adaptively filtering noise. Regarding computational efficiency, Table 3 shows that PRISM-Net requires 11.33M parameters and 0.75 GFLOPs. Compared with heavy 3D-convolutional models that require over 10 GFLOPs, the proposed method achieves superior accuracy with significantly lower resource consumption. Finally, t-SNE visualization in Fig. 5 shows that PRISM-Net produces more compact intra-class distributions and clearer inter-class margins demonstrating improved feature discriminability. Conclusions This research demonstrates that the integration of the biomechanical characteristics of human motion into deep learning architectures improves the robustness of radar gait recognition. The proposed PRISM-Net framework effectively decouples motion components and suppresses clothing-induced noise through physics-aware frequency reconstruction and adaptive weighting. The high recognition accuracy and low computational complexity observed in the MMRGait-1.0 dataset suggest that this approach is suitable for real-time security applications on edge-computing devices.
- Millimeter-wave radar,
- Gait recognition,
- Micro-Doppler effect,
- Attention mechanism,
- Deep Learning

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References(24)

References

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