Biometrics-Based Mobile User Authentication for the Elderly: Accessibility, Performance, and Method Design

Abstract

Assistive technology is extremely important for maintaining and improving the elderly’s quality of life. Biometrics-based mobile user authentication (MUA) methods have witnessed rapid development in recent years owing to their usability and security benefits. However, there is a lack of a comprehensive review of such methods for the elderly. The primary objective of this research is to analyze the literature on state-of-the-art biometrics-based MUA methods via the lens of elderly users’ accessibility needs. In addition, conducting an MUA user study with elderly participants faces significant challenges, and it remains unclear how the performance of the elderly compares with non-elderly users in biometrics-based MUA. To this end, this research summarizes method design principles for user studies involving elderly participants and reveals the performance of elderly users relative to non-elderly users in biometrics-based MUA. The article also identifies open research issues and provides suggestions for the design of effective and accessible biometrics-based MUA methods for the elderly.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is partially supported by two awards from the National Science Foundation [Award #s: CNS 1917537 and SES 1912898]. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the above funding agency.

Notes on contributors

Kanlun Wang

Kanlun Wang is a Ph.D. student in the Department of Business Information Systems and Operations Management at the University of North Carolina at Charlotte. His research interests include content moderation, social media analytics, mobile user authentication, and human-computer interaction.

Lina Zhou

Lina Zhou is a full professor in Business Analytics in the Department of Business Information Systems and Operations Management at the University of North Carolina at Charlotte. Her research interests span the areas of social media analytics, deception detection, biomedical informatics, and intelligent mobile interface.

Dongsong Zhang

Dongsong Zhang a Belk Endowed Chair Professor in Business Analytics in the Department of Business Information Systems and Operations Management at the University of North Carolina at Charlotte. His research interests include social media analytics, health IT, mobile HCI, and intelligent decision-making.