| Full text | |
| Author(s): |
Santos, Geise
[1]
;
Pisani, Paulo Henrique
[2]
;
Leyva, Roberto
[3]
;
Li, Chang-Tsun
[4]
;
Tavares, Tiago
[5]
;
Rocha, Anderson
[1]
Total Authors: 6
|
| Affiliation: | [1] Univ Estadual Campinas, Inst Comp, RECOD Lab, Campinas - Brazil
[2] Fed Univ ABC, Ctr Math Comp & Cognit, Santo Andre, SP - Brazil
[3] Univ Essex, Comp Sci & Elect Engn, Colchester, Essex - England
[4] Deakin Univ, Sch Informat Technol, Melbourne, Vic - Australia
[5] Univ Estadual Campinas, Sch Elect & Comp Engn, Campinas - Brazil
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | PATTERN RECOGNITION; v. 106, OCT 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Mobile devices are becoming ubiquitous and being increasingly used for data-sensitive activities such as communication, personal media storage, and banking. The protection of such data commonly relies on passwords and biometric traits such as fingerprints. These methods perform the user authentication sporadically and often require action from the user, which may make them susceptible to spoofing attacks. This scenario can be mitigated if we bring to bear motion-sensing based methods for authentication, which operate continuously and without requiring user action, hence are harder to attack. Such methods could be used allied with traditional authentication methods or on their own. This paper explores this idea in a novel user-agnostic approach for identity verification based on motion traits acquired by mobile sensors. The proposed approach does not require user-specific training before deployment in mobile devices nor does it require any extra sensor in the device. This solution is capable of learning a user profiling manifold from a small user subset and extend it to unknown users. We validated the proposal on two public datasets. The reported experiments demonstrate remarkable results under a cross-dataset protocol and an open-set setup. Moreover, we performed several analyses aiming at answering critical questions of a biometric method and the presented solution. (C) 2020 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |