Privacy-Constrained Biometric System for Non-Cooperative Users

Mohammad N S Jahromi,Sergio Escalera,Gholamreza Anbarjafari,Egils Avots,Kamal Nasrollahi,Pau Buch-Cardona,Thomas B Moeslund

doi:10.3390/e21111033

Abstract

With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.

Highlights

Biometric recognition is the science of identification of individuals based on their biological and behavioral traits [1,2]
Biometric recognition systems have posed new challenges related to personal data protection (e.g., GDPR), which is not often considered by conventional recognition methods [4]
In Deep Learning, we find the best setup classification results (84.4% accuracy) when analyzing the appearance per subject sequence modality—that is, when we use the whole sequence of frames per subject to determine the resulting class

Summary

Introduction

Biometric recognition is the science of identification of individuals based on their biological and behavioral traits [1,2]. In the design of a biometrics-based recognition or authentication system, different issues, heavily related to the specific application, must be taken into account. Besides the choice of the biometrics to employ, many other issues must be considered in the design stage. Biometric recognition systems have posed new challenges related to personal data protection (e.g., GDPR), which is not often considered by conventional recognition methods [4]. The use of biometrics data may reveal sensitive information about a person’s personality and health, which can be stored, processed, and distributed without the user’s consent [5]. GDPR has a distinct category of personal data protection that defines

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Conclusion