WiFi-Based Human Identification via Convex Tensor Shapelet Learning

Zou, Han; Zhou, Yuxun; Yang, Jianfei; Gu, Weixi; Xie, Lihua; Spanos, Costas J.

doi:10.1609/aaai.v32i1.11497

Cited by 55 publications

(19 citation statements)

References 21 publications

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“…In contrast, by feeding a massive amount of data into machine learning [22] or deep learning networks, [9], [5], learning based achieve remarkable performances in complicated sensing tasks. Various deep neural networks are designed to enable many applications including activity recognition [23], gesture recognition [9], human identification [11], [12], [24], and people counting [13], [14]. Though deep learning models have a strong ability of function approximation, they require tremendous labeled data that is expensive to collect and suffer from the negative effect of distribution shift caused by environmental dynamics [25].…”

Section: Models and Datamentioning

confidence: 99%

“…By analysing the patterns of its wireless signal, today's AP has evolved beyond a pure WiFi router, but is also widely used as a type of 'sensor device' to enable new services for human sensing. Particularly, recent studies have found that WiFi signals in the form of Channel State Information (CSI) [1], [2] are extremely promising for a variety of devicefree human sensing tasks, such as occupancy detection [3], activity recognition [4], [5], [6], [7], fall detection [8], gesture recognition [9], [10], human identification [11], [12], and people counting [13], [14]. Unlike the coarse-grained received signal strengths, WiFi CSI records more fine-grained information about how a signal propagates between WiFi J. Yang, X. Chen, D. Wang, H. Zou and L. Xie are with the School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore (e-mail: {yang0478,dazhuo001,zouh0005,elhxie}@ntu.edu.sg).…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Yang¹,

Chen²,

Wang³

et al. 2022

Preprint

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WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we highlight the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/CHENXINYANsg/WiFi-CSI-Sensing-Benchmark.

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Section: Models and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Yang¹,

Chen²,

Wang³

et al. 2022

Preprint

Self Cite

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“…Therefore, gait recognition can be achieved by extracting these interrelated salient features. The advantage of gait-based human identification is that gait can not only be captured at a longer distance but is also difficult to imitate, which has attracted many researchers to employ various sensors for gait recognition [7], [8].…”

Section: Introductionmentioning

confidence: 99%

GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning

Deng

Yang

Yuan

et al. 2023

IEEE Internet Things J.

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As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this paper, we propose a novel multimodal gait recognition method, namely GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, Channel State Information (CSI) that reflects the multi-path propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a Lightweight Residual Convolution Network (LRCN) as the backbone network, and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects.

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“…To eliminate issues such as privacy and weak light, the researchers propose using WiFi for identity recognition, based on the principle that each person's unique body features and gait characteristics lead to different channel state information (CSI) patterns to recognize subtle differences between people [10]. [11] combines the convolutional layer with the LSTM layer and proposes a simple and effective deep learning method to realize automatic identification of people by WiFi.…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition Using Spatio-Temporal Information of 3D Point Cloud via Millimeter Wave Radar

Zhao

Luo

et al. 2022

Wireless Communications and Mobile Computing

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Gait recognition is one of the crucial methods in identity recognition, which has a wide range of applications in many fields, such as smart home, smart office, and health monitoring. The camera is the most mainstream traditional solution. But the camera is difficult to maintain stable performance in the dark, low light, and bad weather conditions. In addition, privacy leakage is also one of the important issues that people worry about. In contrast, as the latest research progress in gait recognition, millimeter wave radar can not only protect people’s privacy, but also maintain normal perception performance in dark conditions. In this paper, we propose a system for gait recognition named MTPGait using spatio-temporal information via millimeter wave radar. We specially design a neural network that can extract multiscale spatio-temporal features along space and time dimensions of 3D point cloud concisely and efficiently. We use LSTM to design the context flow of local and global time and space, fusing local and global spatio-temporal features. In addition, we construct and release a millimeter wave radar 3D point cloud data set, which consists of 960-minute gait data of 40 volunteers. Using the data set, we evaluate the system and compare it with four state-of-the-art algorithms. The experimental results show that MTPGait is able to achieve 96.7% recognition accuracy in a single-person scene on fixed route and 90.2% recognition accuracy when two people coexist, while none of the existing methods is more than 90% recognition accuracy in either scenario.

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WiFi-Based Human Identification via Convex Tensor Shapelet Learning

Cited by 55 publications

References 21 publications

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning

Gait Recognition Using Spatio-Temporal Information of 3D Point Cloud via Millimeter Wave Radar

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