@article{MTMT:33892456,
	title = {A Hybrid Method of Feature Extraction for Signatures Verification Using CNN and HOG a Multi-Classification Approach},
	url = {https://m2.mtmt.hu/api/publication/33892456},
	author = {Alsuhimat, Fadi Mohammad and Mohamad, Fatma Susilawati},
	doi = {10.1109/ACCESS.2023.3252022},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {11},
	unique-id = {33892456},
	issn = {2169-3536},
	abstract = {The offline signature verification system's feature extraction stage is regarded as crucial and has a significant impact on how well these systems perform because the quantity and calibration of the features that are extracted determine how well these systems can distinguish between authentic and fake signatures. In this study, we introduced a hybrid method for extracting features from signature images, wherein a Convolutional Neural Network (CNN) and Histogram of Oriented Gradients (HOG) were used, followed by the feature selection algorithm (Decision Trees) to identify the key features. Finally, the CNN and HOG methods were combined. Three classifiers were employed to evaluate the efficacy of the hybrid method (long short-term memory, support vector machine, and K-nearest Neighbor). The experimental findings indicated that our suggested model executed satisfactorily in terms of efficiency and predictive ability, with accuracies of (95.4%, 95.2%, and 92.7%) the UTSig dataset, and (93.7%, 94.1%, and 91.3%, respectively) with the CEDAR dataset. This accuracy is deemed to be of high significance, particularly given that we checked skilled forged signatures that are more difficult to recognize than other forms of forged signatures like (simple or opposite).},
	keywords = {Feature extraction; Histograms; CNN; Machine learning algorithms; Biometrics (access control); Convolutional neural networks; Deep learning; Deep learning; HOG; forgery; Offline signature verification},
	year = {2023},
	eissn = {2169-3536},
	pages = {21873-21882}
}

@article{MTMT:32394123,
	title = {Off-line signature verification using elementary combinations of directional codes from boundary pixels},
	url = {https://m2.mtmt.hu/api/publication/32394123},
	author = {Ajij, Md and Pratihar, Sanjoy and Nayak, Soumya Ranjan and Hanne, Thomas and Roy, Diptendu Sinha},
	doi = {10.1007/s00521-021-05854-6},
	journal-iso = {NEURAL COMPUT APPL},
	journal = {NEURAL COMPUTING & APPLICATIONS},
	unique-id = {32394123},
	issn = {0941-0643},
	abstract = {Verifying the genuineness of official documents, such as bank checks, certificates, contract forms, bonds, etc., remains a challenging task when it comes to accuracy and robustness. Here, the genuineness is related to the degree of match of the signature contained in the documents relating to the original signatures of the authorized person. Signatures of authorized persons are considered known in advance. In this paper, a novel feature set is introduced based on quasi-straightness of boundary pixel runs for signature verification. We extract the quasi-straight line segments using elementary combinations of the directional codes from the signature boundary pixels and subsequently we obtain the feature set from various quasi-straight line classes. The quasi-straight line segments provide a blending of straightness and small curvatures resulting in a robust feature set for the verification of signatures. We have used Support Vector Machine (SVM) for classification and have shown results on standard signature datasets like CEDAR (Center of Excellence for Document Analysis and Recognition) and GPDS-100 (Grupo de Procesado Digital de la Senal). The results establish how the proposed method outperforms the existing state of the art.},
	keywords = {Biometrics; signature verification; Person identification; Quasi-straightness},
	year = {2021},
	eissn = {1433-3058},
	orcid-numbers = {Nayak, Soumya Ranjan/0000-0002-4155-884X}
}

@article{MTMT:31076867,
	title = {Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures},
	url = {https://m2.mtmt.hu/api/publication/31076867},
	author = {Diaz, Moises and Ferrer, Miguel A. and Ramalingam, Soodamani and Guest, Richard},
	doi = {10.1109/TIFS.2019.2924195},
	journal-iso = {IEEE T INF FOREN SEC},
	journal = {IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY},
	volume = {15},
	unique-id = {31076867},
	issn = {1556-6013},
	abstract = {In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.},
	keywords = {Biometrics; off-line signature verification; no reference signatures; feature-distance matrix; signature complexity},
	year = {2020},
	eissn = {1556-6021},
	pages = {487-499},
	orcid-numbers = {Diaz, Moises/0000-0003-3878-3867}
}

@article{MTMT:31076868,
	title = {A Perspective Analysis of Handwritten Signature Technology},
	url = {https://m2.mtmt.hu/api/publication/31076868},
	author = {Diaz, Moises and Ferrer, Miguel A. and Impedovo, Donato and Malik, Muhammad Imran and Pirlo, Giuseppe and Plamondon, Rejean},
	doi = {10.1145/3274658},
	journal-iso = {ACM COMPUT SURV},
	journal = {ACM COMPUTING SURVEYS},
	volume = {51},
	unique-id = {31076868},
	issn = {0360-0300},
	abstract = {Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.},
	keywords = {surveys; Biometrics; Offline and online handwritten signature; automatic signature verification},
	year = {2019},
	eissn = {1557-7341},
	orcid-numbers = {Diaz, Moises/0000-0003-3878-3867; Impedovo, Donato/0000-0002-9285-2555}
}

@article{MTMT:27566330,
	title = {A Mobile Computing Method Using CNN and SR for Signature Authentication with Contour Damage and Light Distortion},
	url = {https://m2.mtmt.hu/api/publication/27566330},
	author = {Wang, Mei and Zhai, Ke and Liu, Chi Harold and Li, Yujie},
	doi = {10.1155/2018/5412925},
	journal-iso = {WIREL COMMUN MOB COM},
	journal = {WIRELESS COMMUNICATIONS & MOBILE COMPUTING},
	unique-id = {27566330},
	issn = {1530-8669},
	year = {2018},
	eissn = {1530-8677}
}

@article{MTMT:26220159,
	title = {Efficient approach for iris recognition},
	url = {https://m2.mtmt.hu/api/publication/26220159},
	author = {Hamouchene, Izem and Aouat, Saliha},
	doi = {10.1007/s11760-016-0900-y},
	journal-iso = {SIGNAL IMAGE VIDEO PROCES},
	journal = {SIGNAL IMAGE AND VIDEO PROCESSING},
	volume = {10},
	unique-id = {26220159},
	issn = {1863-1703},
	year = {2016},
	eissn = {1863-1711},
	pages = {1361-1367}
}

@article{MTMT:26034458,
	title = {New off-line Handwritten Signature Verification method based on Artificial Immune Recognition System},
	url = {https://m2.mtmt.hu/api/publication/26034458},
	author = {Serdouk, Yasmine and Nemmour, Hassiba and Chibani, Youcef},
	doi = {10.1016/j.eswa.2016.01.001},
	journal-iso = {EXPERT SYST APPL},
	journal = {EXPERT SYSTEMS WITH APPLICATIONS},
	volume = {51},
	unique-id = {26034458},
	issn = {0957-4174},
	year = {2016},
	eissn = {1873-6793},
	pages = {186-194}
}

@article{MTMT:26390215,
	title = {SAR: Stroke Authorship Recognition},
	url = {https://m2.mtmt.hu/api/publication/26390215},
	author = {Shaheen, Sara and Rockwood, Alyn and Ghanem, Bernard},
	doi = {10.1111/cgf.12733},
	journal-iso = {COMPUT GRAPH FORUM},
	journal = {COMPUTER GRAPHICS FORUM},
	volume = {35},
	unique-id = {26390215},
	issn = {0167-7055},
	year = {2016},
	eissn = {1467-8659},
	pages = {73-86}
}

@article{MTMT:24902777,
	title = {The Off-line Signature Verification Based on Structural Similarity},
	url = {https://m2.mtmt.hu/api/publication/24902777},
	author = {Favorskaya, Margarita and Baranov, Roman},
	doi = {10.3233/978-1-61499-405-3-421},
	editor = {NevesSilva, R and Tshirintzis, GA and Uskov, V and Howlett, RJ and Jain, LC},
	journal-iso = {FRONT ARTIF INTELL APPL},
	journal = {FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS},
	volume = {262},
	unique-id = {24902777},
	issn = {0922-6389},
	year = {2014},
	eissn = {1879-8314},
	pages = {421-430}
}

@CONFERENCE{MTMT:25056249,
	title = {Offline handwritten signature verification system using a supervised neural network approach},
	url = {https://m2.mtmt.hu/api/publication/25056249},
	author = {Jarad, M and Al-Najdawi, N and Tedmori, S},
	booktitle = {2014 6th International Conference on Computer Science and Information Technology, CSIT 2014},
	doi = {10.1109/CSIT.2014.6805999},
	publisher = {IEEE Computer Society},
	unique-id = {25056249},
	year = {2014},
	pages = {189-195}
}

@article{MTMT:24767048,
	title = {Off-Line Signature Verification Based on Local Structural Pattern Distribution Features},
	url = {https://m2.mtmt.hu/api/publication/24767048},
	author = {Wen, Jing MoHan Chen and JiaXin, Ren},
	journal-iso = {PATTERN RECOGN},
	journal = {PATTERN RECOGNITION},
	volume = {484},
	unique-id = {24767048},
	issn = {0031-3203},
	year = {2014},
	eissn = {1873-5142},
	pages = {499-507}
}

@mastersthesis{MTMT:24767050,
	title = {Variability of Handwriting Biomechanics. A Focus on Grip Kinetics during Signature Writing},
	url = {https://m2.mtmt.hu/api/publication/24767050},
	author = {Ghali, Bassma},
	unique-id = {24767050},
	year = {2013}
}

@{MTMT:24561060,
	title = {ICDAR2013 Competitions on Signature Verification and Writer Identification for On- and Offline Skilled Forgeries (SigWiComp2013)},
	url = {https://m2.mtmt.hu/api/publication/24561060},
	author = {Malik, MI and Liwicki, M and Alewijnse, L and Ohyama, W and Blumenstein, M and Found, B},
	booktitle = {PROC INT CONF DOC},
	doi = {10.1109/ICDAR.2013.220},
	publisher = {Institute of Electrical and Electronics Engineers},
	unique-id = {24561060},
	year = {2013},
	pages = {1477-1483}
}

@CONFERENCE{MTMT:24767047,
	title = {Questioned Documents},
	url = {https://m2.mtmt.hu/api/publication/24767047},
	author = {Partouche, CNE Franck and Rosny, Sous Bois},
	booktitle = {17th Interpol International Forensic Science Managers Symposium},
	unique-id = {24767047},
	year = {2013},
	pages = {854-897}
}