Logo des Repositoriums
 
Konferenzbeitrag

Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen

Vorschaubild nicht verfügbar

Volltext URI

Dokumententyp

Text/Conference Paper

Zusatzinformation

Datum

2022

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Verlag

ACM

Zusammenfassung

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

Beschreibung

Schrapel, Maximilian; Grannemann, Dennis; Rohs, Michael (2022): Sign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen. Mensch und Computer 2022 - Tagungsband. DOI: 10.1145/3543758.3543764. New York: ACM. pp. 209-218. MCI-SE04: Artificial Intelligence. Darmstadt. 4.-7. September 2022

Zitierform

Tags