Deep Learning-Based Gait Recognition Using Smartphones in the Wild

TL;DR

This paper introduces a deep learning approach for gait recognition using smartphones in unconstrained, real-world conditions, achieving high accuracy for person identification and authentication.

Contribution

It presents a hybrid deep neural network model that effectively captures gait features from inertial data collected in the wild, improving recognition performance over traditional methods.

Findings

Achieves over 93.5% accuracy in person identification.

Achieves over 93.7% accuracy in gait authentication.

Validates effectiveness on two real-world smartphone datasets.

Abstract

Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes gait data convenient and inexpensive to collect. In this paper, we study gait recognition using smartphones in the wild. In contrast to traditional methods, which often require a person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under unconstrained conditions without knowing when, where, and how the user walks. To obtain good person identification and authentication performance, deep-learning techniques are presented to learn and model the gait biometrics based on walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones for a total of 118 subjects are used for evaluations. The experiments show that the proposed method achieves higher than 93.5\% and 93.7\% accuracies in person identification and authentication, respectively.