Gait Authentication using a wearable sensor

Enomoto, Yusuke; Dai, Honghua; Oguchi, Kimio

doi:10.1109/dsr.2011.6026807

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…The gait-based user authentication techniques use the gait features for authentication purposes. In [21], the authors have introduced a gait-based authentication technique. The data is collected using the ankle-worn accelerometer sensor.…”

Section: B Behavioral Biometricmentioning

confidence: 99%

IoTAuth: IoT Sensor Data Analytics for User Authentication Using Discriminative Feature Analysis

et al. 2022

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The revolution of IoT highly impacts on different applications such as remote sensing, smart cities, and remote digital healthcare. People use IoT devices for performing business transactions, daily tasks, and healthcare monitoring. IoT devices generate huge amounts of data assets that have potential applications. Biometrics is a potential application of sensors data. The traditional biometric methods such as PINs, passwords are exposed to numerous attacks such as replication, repeated passwords, etc. Sensors' data-based continuous authentication methods are suitable for maintaining users' privacy and security in mobile IoT systems. Most of the existing authentication methods have applied motion-based sensors for building users' identification profiles. The proposed method uses motion sensors and biomedical sensors for reliable and multi-factor user authentication. In this article, we have introduced an IoT sensors data analytics framework to construct user authentication models. We apply the fiducial points-based feature extraction method data for extracting discriminative features. These features act as unique user profiles for authentication purposes. We have performed a detailed analysis of the proposed approach using the publically available datasets. The experiments elaborate on the effectiveness of IoTauth for improved authentication results.

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Section: B Behavioral Biometricmentioning

confidence: 99%

IoTAuth: IoT Sensor Data Analytics for User Authentication Using Discriminative Feature Analysis

et al. 2022

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“…Gait data gathered by wearable sensors are unique for each person and can be therefore used for authentication purposes. In Terada et al, the authentication algorithm is based on the traveling acceleration at the swing phase. In Mohamed and Cheffena, the impact of the gait to the received signal strength (RSS) was recorded for off‐body channels, and a feed‐forward neural network was exploited to authenticate a person.…”

Section: Introductionmentioning

confidence: 99%

Physical layer authentication of off‐body channels by probabilistic neural networks

Raida

2019

Int J Numerical Modelling

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A probabilistic neural network (PNN) for the authentication of an off‐body channel of a wireless body area network is discussed in this paper. The off‐body channel is authenticated by radio‐frequency fingerprints of on‐body channels. Thanks to a relatively high stationarity of on‐body channels, a reasonable probability of radio‐frequency fingerprinting identification is reached. Radio‐frequency fingerprints are used to train a probabilistic neural network playing the role of an authentication key. The authentication process can be efficiently controlled by setting tolerance zones of the network. Functionality of the concept was proven by computer simulations.

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MMU GASPFA: A COTS multimodal biometric database

Tan

et al. 2013

Pattern Recognition Letters

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Gait Authentication using a wearable sensor

Cited by 6 publications

References 4 publications

IoTAuth: IoT Sensor Data Analytics for User Authentication Using Discriminative Feature Analysis

IoTAuth: IoT Sensor Data Analytics for User Authentication Using Discriminative Feature Analysis

Physical layer authentication of off‐body channels by probabilistic neural networks

MMU GASPFA: A COTS multimodal biometric database

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