GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning. (arXiv:2110.03967v2 [cs.CV] UPDATED)

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) a convolutional
Autoencoder that transforms attributes of the biometric raw data, such …

arxiv biometrics cv learning mobile privacy unsupervised unsupervised learning