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GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning

Delgado-Santos, Paula, Tolosana, Ruben, Guest, Richard, Vera-Rodriguez, Ruben, Deravi, Farzin, Morales, Aythami (2022) GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning. Pattern Recognition Letters, 161 . pp. 30-37. ISSN 0167-8655. (doi:10.1016/j.patrec.2022.07.015) (KAR id:100460)


Numerous studies in the literature have already shown the potential of biometrics on mobile devices for authentication purposes. However, it has been shown that, the learning processes associated to biometric systems might expose sensitive personal information about the subjects. This study proposes GaitPrivacyON, a novel mobile gait biometrics verification approach that provides accurate authentication results while preserving the sensitive information of the subject. It comprises two modules: i) two convolutional Autoencoders with shared weights that transform attributes of the biometric raw data, such as the gender or the activity being performed, into a new privacy-preserving representation; and ii) a mobile gait verification system based on the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a Siamese architecture. The main advantage of GaitPrivacyON is that the first module (convolutional Autoencoders) is trained in an unsupervised way, without specifying the sensitive attributes of the subject to protect. Two experimental studies have been examinated: i) MotionSense and MobiAct databases; and ii) OU-ISIR database. The experimental results achieved suggest the potential of GaitPrivacyON to significantly improve the privacy of the subject while keeping user authentication results higher than 96.6% Area Under the Curve (AUC). To the best of our knowledge, this is the first mobile gait verification approach that considers privacy-preserving methods trained in an unsupervised way.

Item Type: Article
DOI/Identification number: 10.1016/j.patrec.2022.07.015
Uncontrolled keywords: Privacy preserving, Sensitive data, Gait verification, Mobile sensors, Biometrics
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7882.B56 Biometric identification
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Funders: European Union (
Depositing User: Paula Delgado de Santos
Date Deposited: 14 Mar 2023 10:16 UTC
Last Modified: 18 Jul 2023 14:30 UTC
Resource URI: (The current URI for this page, for reference purposes)

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