Abstract
Impulse Radio Ultra-WideBand (IR-UWB) radar has great potential in personnel identification due to its characteristics of low power consumption and high time resolution. Several radar-based personnel identification methods collect gait data and use Convolutional Neural Network (CNN) for training and classification. However, gait data methods require each person to move in a specific way, which can be improved by using vital signs data instead including body shape and micro movement. In this paper, A MultI-scale Neural network based impulse radio ultra-wideband radar Indoor Personnel Identification method (MINIPI) is proposed, which can extract vital signs of radar signals and map them into identity information. The input of the network is a matrix reshaped from the maximum energy waveform in a radar signal slice and the corresponding vital signs. MINIPI uses a three-layer structure to extract features of three scales from the matrix. These features are concatenated, then input into an attention module and a fully connected layer to achieve identification. To evaluate the performance of MINIPI, we set up a dataset containing 10 persons indoors. The experiment result shows that the accuracy of MINIPI is 94.8% among these 10 persons, which is better than the gait data method using CNN by 3%. The indoor radar signal dataset and the source code are available at https://github.com/bupt-uwb/MINIPI.
L. Meng and J. Zhang—These authors contributed equally to this work and should be considered co-first authors.
This work was supported by the National Natural Science Foundation of China (Grant No. 62176024) and project A02B01C01-201916D2.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Li, H.-B., Takizawa, K., Kagawa, T., Kojima, F., Miura, R.: Improvement on localization accuracy of IR-UWB by adapting time bias inner transceiver. In: 2019 International Conference on Computing, Networking and Communications (ICNC), 2019, pp. 116–120 (2019). https://doi.org/10.1109/ICCNC.2019.8685621
Chen, V.: The Micro-Doppler Effect in Radar. Second Edition, Artech (2019)
Ranjan, R., et al.: A fast and accurate system for face detection, identification, and verification. IEEE Trans. Biometrics Behav. Identity Sci.IEEE Trans. Biometrics Behav. IdentitySci. 1(2), 82–96 (2019). https://doi.org/10.1109/TBIOM.2019.2908436
Tshomo, K., Tshering, K., Gyeltshen, D., Yeshi, J., Muramatsu, K.: Dual door lock system using radio-frequency identification and fingerprint recognition. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019, pp. 1–5. https://doi.org/10.1109/I2CT45611.2019.9033636
Papanastasiou, V.S., Trommel, R.P., Harmanny, R.I.A., Yarovoy, A.: Deep Learning-based identification of human gait by radar micro-Doppler measurements. In: 2020 17th European Radar Conference (EuRAD), 2021, pp. 49–52 (2021). https://doi.org/10.1109/EuRAD48048.2021.00024
Saho, K., Shioiri, K., Inuzuka, K.: Accurate person identification based on combined sit-to-stand and stand-to-sit movements measured using doppler radars. IEEE Sensors J. 21(4), 4563–4570 (2021). https://doi.org/10.1109/JSEN.2020.3032960
Z. Xia, G. Ding, H. Wang and F. Xu, Person Identification with Millimeter-Wave Radar in Realistic Smart Home Scenarios, in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, Art no. 3509405, https://doi.org/10.1109/LGRS.2021.3117001
Vandersmissen, B., et al.: Indoor person identification using a low-power FMCW radar. IEEE Trans. Geosci. Remote Sens. 56(7), 3941–3952 (2018). https://doi.org/10.1109/TGRS.2018.2816812
Connor, P., Ross, A.: Biometric recognition by gait: a survey of modalitiesand features. Comput. Vis. Image Underst. 167, 1–27 (2018)
Ni, Z., Huang, B.: Human identification based on natural gait micro-Doppler signatures using deep transfer learning. IET Radar, Sonar Navigat. 14(10), 1640–1646 (2020). https://doi.org/10.1049/iet-rsn.2020.0183
Ni, Z., Huang, B.: Open-set human identification based on gait radar micro-doppler signatures. IEEE Sens. J. 21(6), 8226–8233 (2021). https://doi.org/10.1109/JSEN.2021.3052613
Duan, Z., Liang, J.: Non-contact detection of vital signs using a UWB radar sensor. IEEE Access 7, 36888–36895 (2019). https://doi.org/10.1109/ACCESS.2018.2886825
Shen, H., et al.: Respiration and heartbeat rates measurement based on autocorrelation using IR-UWB radar. IEEE Trans. Circuits Syst. II Express Briefs 65(10), 1470–1474 (2018). https://doi.org/10.1109/TCSII.2018.2860015
Yang, X., Ding, Y., Zhang, X., Zhang, L.: Spatial-temporal-circulated GLCM and physiological features for in-vehicle people sensing based on IR-UWB Radar. IEEE Trans. Instrumentation Measur. 71, 1–13, 2022, Art no. 8502113, https://doi.org/10.1109/TIM.2022.3165808
Yang, X., Yin, W., Li, L., Zhang, L.: Dense people counting using IR-UWB radar with a hybrid feature extraction method. IEEE Geosci. Remote Sens. Lett. 16(1), 30–34 (2019). https://doi.org/10.1109/LGRS.2018.2869287
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation[J]. Springer International Publishing: Lecture Notes in Computer Science(), vol 9351. Springer, Cham. (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Li, X., Jiang, Y., Li, M., Yin, S.: Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans. Industr. Inf. 17(3), 1958–1967 (2021). https://doi.org/10.1109/TII.2020.2993842
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Meng, L. et al. (2022). MINIPI: A MultI-scale Neural Network Based Impulse Radio Ultra-Wideband Radar Indoor Personnel Identification Method. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-18910-4_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-18909-8
Online ISBN: 978-3-031-18910-4
eBook Packages: Computer ScienceComputer Science (R0)