Gait-based person and gender recognition using micro-doppler signatures

Garreau, Guillaume; Andreou, Charalambos M.; Andreou, Andreas G.; Georgiou, Julius; Durá-Bernal, Salvador; Wennekers, Thomas; Denham, Susan

doi:10.1109/biocas.2011.6107823

Cited by 24 publications

(13 citation statements)

References 11 publications

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“…Conversely, mainly data driven models are studied for gait-based identification. In [6] k-means and k-NN clustering is used on thirteen subjects with an accuracy ranging from 92.4% to 100%. The authors of [17] also apply k-NN along with two manual engineered features and K. Kalgaonkar and B. Raj obtained an accuracy of 90% by using a Gaussian mixture model (GMM) [9].…”

Section: Related Workmentioning

confidence: 99%

Structured Inference Networks Using High-Dimensional Sensors for Surveillance Purposes

Polfliet

Knudde

Vandersmissen

et al. 2018

Engineering Applications of Neural Networks

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Video cameras are arguably the world's most used sensors for surveillance systems. They give a highly detailed representation of a situation that is easily interpreted by both humans and computers. However, these representations can lose part of their representational value when being recorded in less than ideal circumstances. Bad weather conditions, low-light illumination or concealing objects can make the representation more opaque. A radar sensor is a potential solution for these situations, since it is unaffected by the light intensity and can sense through most concealing objects. In this paper, we investigate the performance of a structured inference network on data of a low-power radar device. A structured inference network applies automated feature extraction by creating a latent space out of which the observations can be reconstructed. A classification model can then be trained on this latent space. This methodology allows us to perform experiments for both person identification and action recognition, resulting in competitive error rates ranging from 0% to 6.5% for actions recognition and 10% to 12% for person identification. Furthermore, the possibility of a radar sensor being used as a complement to a camera sensor is investigated.

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Section: Related Workmentioning

confidence: 99%

Structured Inference Networks Using High-Dimensional Sensors for Surveillance Purposes

Polfliet

Knudde

Vandersmissen

et al. 2018

Engineering Applications of Neural Networks

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“…The decomposition of human micro‐Doppler signatures into human body parts through micro‐Doppler signatures leads to the recognition of human activities and may even lead to a weak biometric. A micro‐Doppler system and efficient classifier were demonstrated to identify different individuals and gender [90]. An interferometric radar was shown to have advantages in the case of slow‐moving humans for classification [91].…”

Section: Review Of Micro‐doppler Effect In Radarmentioning

confidence: 99%

Review of micro‐Doppler signatures

Tahmoush

2015

IET Radar, Sonar & Navigation

117

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“…Sensing based on these waves allows us to remotely obtain detailed velocity information without requiring sensors in the users. Although ultrasound micro-Doppler signatures of gait are effective for person identification [15], [16], ultrasound waves are strongly attenuated in air, thus undermining their measurement robustness for security systems at relatively long distances.…”

Section: Introductionmentioning

confidence: 99%

Accurate Person Identification Based on Combined Sit-to-Stand and Stand-to-Sit Movements Measured Using Doppler Radars

2021

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This paper demonstrates the identification of 10 persons with 99% accuracy achieved by combining micro-Doppler signatures of sit-to-stand and stand-to-sit movements. Data from these movements are measured using two radars installed above and behind the person. Images of Doppler spectrograms generated using the measured data are combined and input to a convolutional neural network. Experimental results show the significantly better accuracy of the proposed method compared with conventional methods that do not perform data combination. The accuracy of identifying 10 participants having similar ages and physical features was 96-99%, despite the relatively small training set (number of training samples: 50-90 Doppler radar images per person). These results suggest that combining sit-to-stand and stand-to-sit movements provides sufficient information for accurate person identification and such information can be remotely acquired using Doppler radar measurements.

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Gait-based person and gender recognition using micro-doppler signatures

Cited by 24 publications

References 11 publications

Structured Inference Networks Using High-Dimensional Sensors for Surveillance Purposes

Structured Inference Networks Using High-Dimensional Sensors for Surveillance Purposes

Review of micro‐Doppler signatures

Accurate Person Identification Based on Combined Sit-to-Stand and Stand-to-Sit Movements Measured Using Doppler Radars

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