WristConduct: Biometric User Authentication Using Bone Conduction at the Wrist
| dc.contributor.author | Sehrt, Jessica | |
| dc.contributor.author | Lu, Feng Yi | |
| dc.contributor.author | Husske, Leonard | |
| dc.contributor.author | Roesler, Anton | |
| dc.contributor.author | Schwind, Valentin | |
| dc.contributor.editor | Mühlhäuser, Max | |
| dc.contributor.editor | Reuter, Christian | |
| dc.contributor.editor | Pfleging, Bastian | |
| dc.contributor.editor | Kosch, Thomas | |
| dc.contributor.editor | Matviienko, Andrii | |
| dc.contributor.editor | Gerling, Kathrin|Mayer, Sven | |
| dc.contributor.editor | Heuten, Wilko | |
| dc.contributor.editor | Döring, Tanja | |
| dc.contributor.editor | Müller, Florian | |
| dc.contributor.editor | Schmitz, Martin | |
| dc.date.accessioned | 2022-08-31T09:42:58Z | |
| dc.date.available | 2022-08-31T09:42:58Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Biometric user authentication is an important factor to ensure security and privacy for personal devices. While many devices such as smartphones or laptops can be unlocked based on biometric data, smartwatches or other wrist-worn mobile devices still rely on knowledge-based schemes such as PINs or passwords. In a proof-of-concept study with 24 participants, we show that it is possible to identify individuals using sound waves passing through the wrist bones using a bone conduction speaker and a laryngophone (microphone). We tested support vector machines (SVMs) and artificial neural networks (ANNs) for binary classification. Using ANNs our method shows an authentication accuracy of 98.7%. We discuss the implications of integrating our approach into future devices and contribute with our findings in doing the first step for continuous passive user authentication at the wrist. | en |
| dc.description.uri | https://dl.acm.org/doi/10.1145/3543758.3547542 | en |
| dc.identifier.doi | 10.1145/3543758.3547542 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39236 | |
| dc.language.iso | en | |
| dc.publisher | ACM | |
| dc.relation.ispartof | Mensch und Computer 2022 - Tagungsband | |
| dc.relation.ispartofseries | Mensch und Computer | |
| dc.subject | User Authentication | |
| dc.subject | Biometrics | |
| dc.subject | Wrist-Worn Device | |
| dc.subject | Bone Conduction | |
| dc.subject | Machine Learning | |
| dc.title | WristConduct: Biometric User Authentication Using Bone Conduction at the Wrist | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 365 | |
| gi.citation.publisherPlace | New York | |
| gi.citation.startPage | 361 | |
| gi.conference.date | 4.-7. September 2022 | |
| gi.conference.location | Darmstadt | |
| gi.conference.sessiontitle | MCI-POSTER | |
| gi.document.quality | digidoc |