Gait Classification and Identity Authentication Using CNN

Yuan, Wei; Zhang, Linxuan

doi:10.1007/978-981-13-2853-4_10

Cited by 7 publications

(1 citation statement)

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“…In [62], cross-view gait recognition was studied using CNNs constructed with contrastive loss and triplet ranking loss, which achieved high performance in person verification and identification. In [60], gait data were firstly extracted by a periodogram-based gait separation algorithm, and gait classification and authentication algorithms were then built on convolutional neural networks.…”

Section: B Deep Learning For Gait Recognitionmentioning

confidence: 99%

Deep Learning-Based Gait Recognition Using Smartphones in the Wild

Zou

Wang

et al. 2020

IEEE Trans.Inform.Forensic Secur.

200

141

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Comparing with other biometrics, gait has advantages of being unobtrusive and difficult to conceal. Inertial sensors such as accelerometer and gyroscope are often used to capture gait dynamics. Nowadays, these inertial sensors have commonly been integrated in smartphones and widely used by average person, which makes it very convenient and inexpensive to collect gait data. In this paper, we study gait recognition using smartphones in the wild. Unlike traditional methods that often require the person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under a condition of unconstraint without knowing when, where, and how the user walks. To obtain a high performance of person identification and authentication, deep-learning techniques are presented to learn and model the gait biometrics from the walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space domain and in the time domain are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones on a total of 118 subjects are used for evaluations. Experiments show that the proposed method achieves over 93.5% and 93.7% accuracy in person identification and authentication, respectively.Index Terms-Gait recognition, inertial sensor, person identification, convolutional neural network, recurrent neural network.

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Section: B Deep Learning For Gait Recognitionmentioning

confidence: 99%