---
_id: '12873'
abstract:
- lang: eng
text: Reliable Banknote Authentication is critical for economic stability. Regarding
everyday use, recent studies implemented successful techniques using banknote
images taken by mobile phone cameras. One challenge in mobile banknote authentication
is that it is impossible to collect images by all series/brands of mobile phones.
In this study, classification models are implemented that are able to generalize
to the samples from a wide number of mobile phone series even though they are
trained with samples from a small group of series. Existing state-of-the-art banknote
authentication approaches train a separate model per sub-image of a banknote,
using the extracted features of that sub-image. A new approach that trains a single
global model on the concatenated features of all the sub-images is presented.
Furthermore, ensemble models that combine Linear Discriminant Analysis and Deep
Neural Networks are employed in order to maximize the accuracy. Implemented techniques
were able to reach up to F1-score of 0.99914 on a Euro banknote data set which
contain images from 16 different mobile-phone series. The results also indicate
that new global model approach can improve the accuracy of the existing banknote
authentication techniques in case of model training with images from restricted/incomplete
phone series and brands.
author:
- first_name: Baris Gün
full_name: Sürmeli, Baris Gün
id: '73806'
last_name: Sürmeli
- first_name: Eugen
full_name: Gillich, Eugen
last_name: Gillich
- first_name: Helene
full_name: Dörksen, Helene
id: '46416'
last_name: Dörksen
citation:
ama: Sürmeli BG, Gillich E, Dörksen H. Generalisation Approach for Banknote Authentication
by Mobile Devices Trained on Incomplete Samples. Vol 14255. (Iliadis Lazaros
, ed.). Springer Nature Switzerland; 2023:332-343. doi:10.1007/978-3-031-44210-0_27
apa: Sürmeli, B. G., Gillich, E., & Dörksen, H. (2023). Generalisation Approach
for Banknote Authentication by Mobile Devices Trained on Incomplete Samples. In Lazaros Iliadis
(Ed.), Artificial Neural Networks and Machine Learning - ICANN 2023 (Vol.
14255, pp. 332–343). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44210-0_27
bjps: 'Sürmeli BG, Gillich E and Dörksen H (2023) Generalisation Approach
for Banknote Authentication by Mobile Devices Trained on Incomplete Samples,
Iliadis Lazaros (ed.). Cham: Springer Nature Switzerland.'
chicago: 'Sürmeli, Baris Gün, Eugen Gillich, and Helene Dörksen. Generalisation
Approach for Banknote Authentication by Mobile Devices Trained on Incomplete Samples.
Edited by Lazaros Iliadis. Artificial Neural Networks and Machine Learning
- ICANN 2023. Vol. 14255. Lecture Notes in Computer Science. Cham: Springer
Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-44210-0_27.'
chicago-de: 'Sürmeli, Baris Gün, Eugen Gillich und Helene Dörksen. 2023. Generalisation
Approach for Banknote Authentication by Mobile Devices Trained on Incomplete Samples.
Hg. von Lazaros Iliadis. Artificial Neural Networks and Machine Learning
- ICANN 2023. Bd. 14255. Lecture Notes in Computer Science. Cham: Springer
Nature Switzerland. doi:10.1007/978-3-031-44210-0_27,
.'
din1505-2-1: 'Sürmeli, Baris Gün ;
Gillich, Eugen ; Dörksen,
Helene ; Iliadis, Lazaros
(Hrsg.): Generalisation Approach for Banknote Authentication by Mobile Devices
Trained on Incomplete Samples, Lecture Notes in Computer Science. Bd.
14255. Cham : Springer Nature Switzerland, 2023'
havard: B.G. Sürmeli, E. Gillich, H. Dörksen, Generalisation Approach for Banknote
Authentication by Mobile Devices Trained on Incomplete Samples, Springer Nature
Switzerland, Cham, 2023.
ieee: 'B. G. Sürmeli, E. Gillich, and H. Dörksen, Generalisation Approach for Banknote
Authentication by Mobile Devices Trained on Incomplete Samples, vol. 14255.
Cham: Springer Nature Switzerland, 2023, pp. 332–343. doi: 10.1007/978-3-031-44210-0_27.'
mla: Sürmeli, Baris Gün, et al. “Generalisation Approach for Banknote Authentication
by Mobile Devices Trained on Incomplete Samples.” Artificial Neural Networks
and Machine Learning - ICANN 2023, edited by Lazaros Iliadis, vol. 14255,
Springer Nature Switzerland, 2023, pp. 332–43, https://doi.org/10.1007/978-3-031-44210-0_27.
short: B.G. Sürmeli, E. Gillich, H. Dörksen, Generalisation Approach for Banknote
Authentication by Mobile Devices Trained on Incomplete Samples, Springer Nature
Switzerland, Cham, 2023.
ufg: 'Sürmeli, Baris Gün/Gillich, Eugen/Dörksen, Helene: Generalisation Approach
for Banknote Authentication by Mobile Devices Trained on Incomplete Samples, Bd.
14255, hg. von Iliadis, Lazaros , Cham 2023 (Lecture Notes in Computer Science).'
van: 'Sürmeli BG, Gillich E, Dörksen H. Generalisation Approach for Banknote Authentication
by Mobile Devices Trained on Incomplete Samples. Iliadis Lazaros , editor. Artificial
Neural Networks and Machine Learning - ICANN 2023. Cham: Springer Nature Switzerland;
2023. (Lecture Notes in Computer Science; vol. 14255).'
conference:
end_date: 2023-09-29
location: Heraklion, GREECE
name: 32nd International Conference on Artificial Neural Networks (ICANN)
start_date: 2023-09-26
date_created: 2025-04-28T14:26:30Z
date_updated: 2025-06-26T07:55:20Z
department:
- _id: DEP5023
doi: 10.1007/978-3-031-44210-0_27
editor:
- first_name: ' Lazaros '
full_name: 'Iliadis, Lazaros '
last_name: Iliadis
intvolume: ' 14255'
language:
- iso: eng
page: 332-343
place: Cham
publication: Artificial Neural Networks and Machine Learning - ICANN 2023
publication_identifier:
eisbn:
- 978-3-031-44210-0
eissn:
- 1611-3349
isbn:
- 978-3-031-44209-4
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature Switzerland
series_title: Lecture Notes in Computer Science
status: public
title: Generalisation Approach for Banknote Authentication by Mobile Devices Trained
on Incomplete Samples
type: conference_editor_article
user_id: '83781'
volume: 14255
year: '2023'
...