@article{MTMT:34034421,
	title = {Systematic Evaluation of Pre-Processing Approaches in Online Signature Verification},
	url = {https://m2.mtmt.hu/api/publication/34034421},
	author = {Saleem, Mohammad and Szücs, Cintia Lia and Kővári, Bence András},
	doi = {10.3233/IDT-220247},
	journal-iso = {INTELL DECIS TECHNOL},
	journal = {INTELLIGENT DECISION TECHNOLOGIES},
	volume = {17},
	unique-id = {34034421},
	issn = {1872-4981},
	year = {2023},
	pages = {655-672},
	orcid-numbers = {Saleem, Mohammad/0000-0002-7274-2711; Kővári, Bence András/0000-0003-1555-640X}
}

@CONFERENCE{MTMT:34025204,
	title = {Származtatott függvénytulajdonságok alkalmazásának szerepe az online aláírás-hitelesítésben},
	url = {https://m2.mtmt.hu/api/publication/34025204},
	author = {Szücs, Cintia Lia and Kővári, Bence András and Charaf, Hassan},
	booktitle = {Proceedings of KEPAF 2023: Képfeldolgozók és Alakfelismerők társaságának 14. konferenciája},
	unique-id = {34025204},
	year = {2023},
	pages = {Paper ID 45},
	orcid-numbers = {Kővári, Bence András/0000-0003-1555-640X}
}

@inproceedings{MTMT:34021323,
	title = {The Usability of Derived Function Features in Online Signature Verification},
	url = {https://m2.mtmt.hu/api/publication/34021323},
	author = {Szücs, Cintia Lia and Kővári, Bence András},
	booktitle = {2022 9TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE, ISCMI},
	doi = {10.1109/ISCMI56532.2022.10068475},
	unique-id = {34021323},
	year = {2022},
	pages = {192-197},
	orcid-numbers = {Kővári, Bence András/0000-0003-1555-640X}
}

@inproceedings{MTMT:33606537,
	title = {The effectiveness of derived local features in online signature verification},
	url = {https://m2.mtmt.hu/api/publication/33606537},
	author = {Szücs, Cintia Lia},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2022 (AACS'22)},
	unique-id = {33606537},
	keywords = {Dynamic time warping; DTW; Online signature verification; Local features; Derived features},
	year = {2022},
	pages = {44-55}
}

@article{MTMT:32842136,
	title = {The Use of Confidence Indicating Prediction Score in Online Signature Verification},
	url = {https://m2.mtmt.hu/api/publication/32842136},
	author = {Heszler, András and Szücs, Cintia Lia and Kővári, Bence András},
	doi = {10.12720/jait.13.3.290-294},
	journal-iso = {J ADV INFORM TECH},
	journal = {JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY},
	volume = {13},
	unique-id = {32842136},
	issn = {1798-2340},
	abstract = {Signature verification is an actively researched area whose goal is to decide whether unknown signatures are genuine or forged. Online signature verification applies signatures captured with an electronic device (digital tablet or pen). Online signatures contain not only spatial information but dynamics as well. There are two types of possible errors, the false prediction as genuine and the false prediction as a forgery. This paper proposes a prediction score as the classification output, which indicates the confidence of the system decision. This approach allows a trade-off between the different error types to create specialized verifiers and construct combined classifiers. This paper presents two types of combined classifiers, pre-filtering classifiers and majority voting classifiers. The proposed approaches are evaluated using the MCYT-100 dataset.},
	year = {2022},
	pages = {290-294}
}

@article{MTMT:32526085,
	title = {SVC-onGoing: Signature Verification Competition},
	url = {https://m2.mtmt.hu/api/publication/32526085},
	author = {Ruben, Tolosana and Ruben, Vera-Rodriguez and Carlos, Gonzalez-Garcia and Julian, Fierrez and Aythami, Morales and Javier, Ortega-Garcia and Juan, Carlos Ruiz-Garcia and Sergio, Romero-Tapiador and Santiago, Rengifo and Miguel, Caruana and Jiajia, Jiang and Songxuan, Lai and Lianwen, Jin and Yecheng, Zhu and Javier, Galbally and Moises, Diaz and Miguel, Angel Ferrer and Marta, Gomez-Barrero and Ilya, Hodashinsky and Konstantin, Sarin and Artem, Slezkin and Marina, Bardamova and Mikhail, Svetlakov and Saleem, Mohammad and Szücs, Cintia Lia and Kővári, Bence András and Falk, Pulsmeyer and Mohamad, Wehbi and Dario, Zanca and Sumaiya, Ahmad and Sarthak, Mishra and Suraiya, Jabin},
	doi = {10.1016/j.patcog.2022.108609},
	journal-iso = {PATTERN RECOGN},
	journal = {PATTERN RECOGNITION},
	volume = {127},
	unique-id = {32526085},
	issn = {0031-3203},
	abstract = {This article presents SVC-onGoing1, an on-going competition for on-line signature verification where re-searchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB(2) and SVC2021_EvalDB(3), and standard experimen-tal protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to eval-uate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously con-sidered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future stud-ies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )},
	keywords = {ONLINE; Biometrics; signature verification; Computer Science, Artificial Intelligence; handwriting; SVC-onGoing; SVC 2021; DeepSignDB},
	year = {2022},
	eissn = {1873-5142},
	orcid-numbers = {Saleem, Mohammad/0000-0002-7274-2711}
}

@inproceedings{MTMT:32525104,
	title = {ICDAR 2021 Competition on On-Line Signature Verification},
	url = {https://m2.mtmt.hu/api/publication/32525104},
	author = {Ruben, Tolosana and Ruben, Vera-Rodriguez and Carlos, Gonzalez-Garcia and Julian, Fierrez and Santiago, Rengifo and Aythami, Morales and Javier, Ortega-Garcia and Juan, Carlos Ruiz-Garcia and Sergio, Romero-Tapiador and Jiajia, Jiang and Songxuan, Lai and Lianwen, Jin and Yecheng, Zhu and Javier, Galbally and Moises, Diaz and Miguel, Angel Ferrer and Marta, Gomez-Barrero and Ilya, Hodashinsky and Konstantin, Sarin and Artem, Slezkin and Marina, Bardamova and Mikhail, Svetlakov and Saleem, Mohammad and Szücs, Cintia Lia and Kővári, Bence András and Falk, Pulsmeyer and Mohamad, Wehbi and Dario, Zanca and Sumaiya, Ahmad and Sarthak, Mishra and Suraiya, Jabin},
	booktitle = {Document Analysis and Recognition – ICDAR 2021},
	doi = {10.1007/978-3-030-86337-1_48},
	unique-id = {32525104},
	abstract = {This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition (https://sites.google.com/view/SVC2021 ), where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases such as DeepSignDB (https://github.com/BiDAlab/DeepSignDB ) and SVC2021_EvalDB (https://github.com/BiDAlab/SVC2021_EvalDB ), and standard experimental protocols. © 2021, Springer Nature Switzerland AG.},
	year = {2021},
	pages = {723-737},
	orcid-numbers = {Saleem, Mohammad/0000-0002-7274-2711}
}

@inproceedings{MTMT:32165631,
	title = {Előfeldolgozási algoritmusok szisztematikus vizsgálata az online aláírás-hitelesítésben},
	url = {https://m2.mtmt.hu/api/publication/32165631},
	author = {Szücs, Cintia Lia and Saleem, Mohammad and Kővári, Bence András and Dócs, Zoltán},
	booktitle = {Képfeldolgozók és Alakfelismerők társaságának 13. konferenciája},
	unique-id = {32165631},
	year = {2021},
	pages = {1},
	orcid-numbers = {Saleem, Mohammad/0000-0002-7274-2711}
}

@inproceedings{MTMT:32118101,
	title = {Online signature verification using non-binary classification},
	url = {https://m2.mtmt.hu/api/publication/32118101},
	author = {Heszler, András and Szücs, Cintia Lia},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2021},
	unique-id = {32118101},
	abstract = {Online signature verification is an actively researched area that uses signatures captured by a digitizing pen or tablet and stored as time series with various features, such as pressures. The goal of a signature verification system is to classify the signatures into forgeries and genuine signatures with low error rates for both cases. This paper proposes a confidence score as the classification output, which allows a trade-off between the different types of errors to optimize the classification, create specialized signature verifiers, and construct combined classifiers. Two types of combined classifiers will be presented, pre-filtering classifiers and majority voting classifiers. The proposed signature verification approaches are evaluated using the publicly available MCYT-100 dataset.},
	keywords = {Confidence score; Ensemble classification; DTW; Online signature verification; MCYT-100},
	year = {2021},
	pages = {204-212}
}

@inproceedings{MTMT:32118073,
	title = {Combination of DTW and global features in on-line signature verification using writer-independent threshold},
	url = {https://m2.mtmt.hu/api/publication/32118073},
	author = {Szücs, Cintia Lia},
	booktitle = {Proceedings of the Automation and Applied Computer Science Workshop 2021},
	unique-id = {32118073},
	abstract = {Handwritten signatures are widely accepted and commonly used in everyday life. Therefore, it is important to be able to decide whether a signature is genuine or a forgery. Automated signature verification enables us to execute this decisionmaking process without human intervention. On-line signature verification uses not only the static image of the signature but also dynamic information about the signing process. This is possible because on-line signatures are captured with electronic devices such as pressure-sensitive pens or digital tablets, which can record additional features besides the coordinates. The dynamic time warping (DTW) algorithm is widely used in on-line signature verification. This work defines an on-line signature verification system that combines DTW and the use of global features. The presented approach deals with a signer-independent threshold.},
	keywords = {DTW; Online signature verification; signing duration; global feature; writer-independent threshold; global threshold},
	year = {2021},
	pages = {262-271}
}