Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen
| dc.contributor.author | Schrapel, Maximilian | |
| dc.contributor.author | Grannemann, Dennis | |
| dc.contributor.author | Rohs, Michael | |
| 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 | 2022-08-31T09:42:53Z | |
| dc.date.available | 2022-08-31T09:42:53Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Although in many cases contracts can be made or ended digitally, laws require handwritten signatures in certain cases. Forgeries are a major challenge with digital contracts, as their validity is not always immediately apparent without forensic methods. Illiteracy or disabilities may result in a person being unable to write their full name. In this case x-mark signatures are used, which require a witness for validity. In cases of suspected fraud, the relationship of the witnesses must be questioned, which involves a great amount of effort. In this paper we use audio and motion data from a digital pen to identify users via handwritten symbols. We evaluated the performance our approach for 19 symbols in a study with 30 participants. We found that x-marks offer fewer individual features than other symbols like arrows or circles. By training on three samples and averaging three predictions we reach a mean F1-score of F 1 = 0.87, using statistical and spectral features fed into SVMs | en |
| dc.description.uri | https://dl.acm.org/doi/10.1145/3543758.3543764 | en |
| dc.identifier.doi | 10.1145/3543758.3543764 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39215 | |
| dc.language.iso | en | |
| dc.publisher | ACM | |
| dc.relation.ispartof | Mensch und Computer 2022 - Tagungsband | |
| dc.relation.ispartofseries | Mensch und Computer | |
| dc.subject | Digital Pens | |
| dc.subject | Signature Authentication | |
| dc.subject | Signing Documents | |
| dc.subject | Pattern Recognition | |
| dc.subject | Handwriting Recognition | |
| dc.subject | Motor Impairments | |
| dc.subject | Accessibility | |
| dc.title | Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 218 | |
| gi.citation.publisherPlace | New York | |
| gi.citation.startPage | 209 | |
| gi.conference.date | 4.-7. September 2022 | |
| gi.conference.location | Darmstadt | |
| gi.conference.sessiontitle | MCI-SE04: Artificial Intelligence | |
| gi.document.quality | digidoc |