DNN-Based Human Face Classification Using 61 GHz FMCW Radar Sensor

Lim, Hae-Seung; Jung, Jaehoon; Lee, Jae-Eun; Park, Hyung‐Min; Lee, Seongwook

doi:10.1109/jsen.2020.2999548

Cited by 16 publications

(8 citation statements)

References 18 publications

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“…For practical applications, extracting relevant data features from the reflection signals is key for millimeter-wave radar. Therefore, researchers have extensively explored data processing methods to extract more detailed features from radar echo signals, such as distance [ 24 ] and angle [ 25 ]. Some studies have used the Orthogonal Matching Pursuit (OMP) algorithm to analyze radar echo signals and obtain micro-Doppler trajectories and then employed neighborhood classifiers to classify gestures [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud

Dang,

Ke,

Hao

et al. 2023

Sensors

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Automatic driving technology refers to equipment such as vehicle-mounted sensors and computers that are used to navigate and control vehicles autonomously by acquiring external environmental information. To achieve automatic driving, vehicles must be able to perceive the surrounding environment and recognize and understand traffic signs, traffic signals, pedestrians, and other traffic participants, as well as accurately plan and control their path. Recognition of traffic signs and signals is an essential part of automatic driving technology, and gesture recognition is a crucial aspect of traffic-signal recognition. This article introduces mm-TPG, a traffic-police gesture recognition system based on a millimeter-wave point cloud. The system uses a 60 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar as a sensor to achieve high-precision recognition of traffic-police gestures. Initially, a double-threshold filtering algorithm is used to denoise the millimeter-wave raw data, followed by multi-frame synthesis processing of the generated point cloud data and feature extraction using a ResNet18 network. Finally, gated recurrent units are used for classification to enable the recognition of different traffic-police gestures. Experimental results demonstrate that the mm-TPG system has high accuracy and robustness and can effectively recognize traffic-police gestures in complex environments such as varying lighting and weather conditions, providing strong support for traffic safety.

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Section: Related Workmentioning

confidence: 99%

mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud

Dang,

Ke,

Hao

et al. 2023

Sensors

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“…In our radar data set, the average identification result for the three faces is about 98.7%. When compared with the Deep neural network (DNN)‐based classifier in [12 ], the classification performance is better on average.…”

Section: Face Identification Using Cnnmentioning

confidence: 99%

Face identification using millimetre‐wave radar sensor data

Kim

Lee²,

Lim

et al. 2020

Electron. lett.

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In this Letter, the authors propose a face identification method using radar sensor data. They use a frequency‐modulated continuous wave radar sensor that utilises a centre frequency of 61 GHz and a bandwidth of 6 GHz. Then, they accumulate radar data by transmitting and receiving radar signals on human faces. Finally, they use a convolutional neural network (CNN) to distinguish radar signals reflected from different human faces. In this network, signals received from multiple antenna elements are synthesised in parallel to make the radar signals into an image that is the input form of the CNN. The accuracy of face recognition through the CNN is >98normal%. In addition, they also collect radar data when the same subjects wear cotton masks. Within their entire dataset, wearing a mask does not significantly affect the accuracy of the radar‐based face recognition method.

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“…In order to apply the mm-wave radar in practical applications, the relevant data features must firstly be extracted from the reflected signal [ 22 , 23 , 24 , 25 , 26 ]. Hence, data processing methods for extracting more detailed features from the reflected signal, such as the features of the distance [ 27 ], velocity [ 28 , 29 ], Radar Cross Section (RCS) value [ 30 ] and angle [ 31 ], are widely investigated. Radar point cloud data not only contains almost all the aforementioned features but also can directly indicate the spatial locations of the targets, and they are receiving more attention.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

Zhang

Geng

Lin

2021

Sensors

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In this paper, comprehensive mPoint, a method for generating 3D (range, azimuth, and elevation) point cloud of human targets using a Frequency-Modulated Continuous Wave (FMCW) signal and Multi-Input Multi-Output (MIMO) millimeter wave radar is proposed. Distinct from the TI-mPoint method proposed by TI technology, a comprehensive mPoint method considering both the static and dynamic characteristics of radar reflected signals is utilized to generate a high precision point cloud, resulting in more comprehensive information of the target being detected. The radar possessing 60–64 GHz FMCW signal with two sets of different dimensional antennas is utilized in order to experimentally verify the results of the methodology. By using the proposed process, the point cloud data of human targets can be obtained based on six different postures of the underlying human body. The human posture cube and point cloud accuracy rates are defined in the paper in order to quantitively and qualitatively evaluate the quality of the generated point cloud. Benefitting from the proposed comprehensive mPoint, evidence shows that the point number and the accuracy rate of the generated point cloud compared with those from the popular TI-mPoint can be largely increased by 86% and 42%, respectively. In addition, the noise level of multipath reflection can be effectively reduced. Moreover, the length of the algorithm running time is only 1.6% longer than that of the previous method as a slight tradeoff.

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DNN-Based Human Face Classification Using 61 GHz FMCW Radar Sensor

Cited by 16 publications

References 18 publications

mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud

mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud

Face identification using millimetre‐wave radar sensor data

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

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