A Look at Feet: Recognizing Tailgating via Capacitive Sensing

Siegmund, Dirk; Dev, Sudeep; Fu, Biying; Scheller, Doreen; Braun, Andreas

doi:10.1007/978-3-319-91131-1_11

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Contigiani et al [ 34 ] utilize a mechanical foot apparatus and no test subjects. Siegmund et al [ 35 ] attempted to reduce tailgating through security checkpoints by detecting people closely transiting through an entryway. Shi et al [ 36 , 37 ] used Convolutional Neural Networks with triboelectric capacitive sensors embedded within the floor to identify either groups of people or individuals with accuracies varying between 85 and 96%.…”

Section: Introductionmentioning

confidence: 99%

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Konings

Alam

Faulkner

et al. 2022

Sensors

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In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.

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Section: Introductionmentioning

confidence: 99%

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Konings

Alam

Faulkner

et al. 2022

Sensors

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Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control

Siegmund

Tran

Wilmsdorff

et al. 2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Acquisition of EFS and Capacitive Measurement Data on Low-Power and Connected IoT Devices

Wilmsdorff

Lenhart

Kirchbuchner

et al. 2022

Communications in Computer and Information Science

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A Look at Feet: Recognizing Tailgating via Capacitive Sensing

Cited by 3 publications

References 8 publications

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Piggybacking Detection Based on Coupled Body-Feet Recognition at Entrance Control

Acquisition of EFS and Capacitive Measurement Data on Low-Power and Connected IoT Devices

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