Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Mensch und Computer 2022 |
| Untertitel | Facing Realities, MuC 2022 - Proceedings |
| Herausgeber/-innen | Max Muhlhauser, Christian Reuter, Bastian Pfleging, Thomas Kosch, Andrii Matviienko, Kathrin Gerling, Sven Mayer, Wilko Heuten, Tanja Doring |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 209-218 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 9781450396905 |
| Publikationsstatus | Veröffentlicht - 15 Sept. 2022 |
| Veranstaltung | 2022 Mensch und Computer Conference: Facing Realities, MuC 2022 - 2022 Conference on Humans and Computers, MuC 2022 - Darmstadt, Deutschland Dauer: 4 Sept. 2022 → 7 Sept. 2022 |
Publikationsreihe
| Name | ACM International Conference Proceeding Series |
|---|
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 F1 = 0.87, using statistical and spectral features fed into SVMs.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Software
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Mensch und Computer 2022: Facing Realities, MuC 2022 - Proceedings. Hrsg. / Max Muhlhauser; Christian Reuter; Bastian Pfleging; Thomas Kosch; Andrii Matviienko; Kathrin Gerling; Sven Mayer; Wilko Heuten; Tanja Doring. Association for Computing Machinery (ACM), 2022. S. 209-218 (ACM International Conference Proceeding Series).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Sign H3re
T2 - 2022 Mensch und Computer Conference: Facing Realities, MuC 2022 - 2022 Conference on Humans and Computers, MuC 2022
AU - Schrapel, Maximilian
AU - Grannemann, Dennis
AU - Rohs, Michael
PY - 2022/9/15
Y1 - 2022/9/15
N2 - 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 F1 = 0.87, using statistical and spectral features fed into SVMs.
AB - 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 F1 = 0.87, using statistical and spectral features fed into SVMs.
KW - accessibility
KW - digital pens
KW - handwriting recognition
KW - motor impairments
KW - pattern recognition
KW - signature authentication
KW - signing documents
UR - http://www.scopus.com/inward/record.url?scp=85139105541&partnerID=8YFLogxK
U2 - 10.1145/3543758.3543764
DO - 10.1145/3543758.3543764
M3 - Conference contribution
AN - SCOPUS:85139105541
T3 - ACM International Conference Proceeding Series
SP - 209
EP - 218
BT - Mensch und Computer 2022
A2 - Muhlhauser, Max
A2 - Reuter, Christian
A2 - Pfleging, Bastian
A2 - Kosch, Thomas
A2 - Matviienko, Andrii
A2 - Gerling, Kathrin
A2 - Mayer, Sven
A2 - Heuten, Wilko
A2 - Doring, Tanja
PB - Association for Computing Machinery (ACM)
Y2 - 4 September 2022 through 7 September 2022
ER -