TY - GEN
T1 - Handwriting features based detection of fake signatures
AU - Akusok, Anton
AU - Espinosa Leal, Leonardo
AU - Bjxc3xb6rk, Kaj Mikael
AU - Lendasse, Amaury
AU - Hu, Renjie
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/6/29
Y1 - 2021/6/29
N2 - Detection of fake signatures is a hard task. In this paper, we present a novel method for detecting trained forgeries using features extracted from sliding windows with different overlaps on a public available dataset of static images of signatures. Using a linear machine learning model named Extreme Learning Machine (ELM), our methodology achieves, in average, an Equal Error Rates (EER) of 2.31% for an overlap of 90%. In line with the state-of-the-art results available in the scientific literature.
AB - Detection of fake signatures is a hard task. In this paper, we present a novel method for detecting trained forgeries using features extracted from sliding windows with different overlaps on a public available dataset of static images of signatures. Using a linear machine learning model named Extreme Learning Machine (ELM), our methodology achieves, in average, an Equal Error Rates (EER) of 2.31% for an overlap of 90%. In line with the state-of-the-art results available in the scientific literature.
KW - biometrics
KW - neural networks
KW - Signature verification
UR - https://www.scopus.com/pages/publications/85109343584
UR - https://www.scopus.com/inward/citedby.url?scp=85109343584&partnerID=8YFLogxK
U2 - 10.1145/3453892.3454003
DO - 10.1145/3453892.3454003
M3 - Conference contribution
AN - SCOPUS:85109343584
T3 - ACM International Conference Proceeding Series
SP - 86
EP - 89
BT - 14th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2021
PB - Association for Computing Machinery
T2 - 14th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2021
Y2 - 29 June 2021 through 1 July 2021
ER -