Dynamic signature verification method based on association of features with similarity measures

Doroz, Rafał; Porwik, Piotr; Orczyk, Tomasz

doi:10.1016/j.neucom.2015.07.026

Cited by 41 publications

(6 citation statements)

References 44 publications

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“…Fisher discriminant analysis (FDA) is then employed with the imaginary as well as real part of the harmonics selected using empirical evaluation to identify the most suitable and appropriate features along with their associated weights. Traditionally, FDA [4] has been employed to serve the purpose but FDA fails to handle multimodality problem within classes. To cope with the multimodality issue, locality preserving projection is proposed [27] but it fails to handle labeled data due to its unsupervised nature.…”

Section: F Dimensionality Reduction For Feature Vectormentioning

confidence: 99%

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Online Signature Verification for Forgery Detection

Rizwan¹,

Aadil²,

Durrani³

et al. 2023

IJACSA

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The increasing trend of using e-versions of document transmission and storage requires the electronic verification of sender/author. This research presents an efficient and robust online handwritten signature verification system targeting verification rates better than the available state-of-theart systems in the presence of skilled forgeries. Fourier analysis is employed on the signatures to represent feature vectors in higher dimensional space followed by Local Fisher Discriminant Analysis to obtain compress representation while enhancing inter-class scatter between signature patterns. Signature modeling is performed using m-mediod-based modeling approach where m-mediods are put on to represent data distribution in each class. Connected component labeling is applied to binarized images of Urdu text to extract ligatures which are separated into primary ligatures and diacritics. Fast Euclidean Distance is used as dis(similarity). A total of 2414 signature samples including skilled forgeries are considered in our study. The evaluation of the proposed system on Japanese signature dataset provided by SigWiComp2013 realized promising results than the competitors.

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Section: F Dimensionality Reduction For Feature Vectormentioning

confidence: 99%

“…Online Signature Verification [4,5] catches the attention of researchers from the past few decades and still is an enduring research area. An online signature is made up of a series of sample points.…”

Section: Introductionmentioning

confidence: 99%

Online Signature Verification for Forgery Detection

Rizwan¹,

Aadil²,

Durrani³

et al. 2023

IJACSA

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“…Therefore, it has many features for classification process [28,49,131,132]. Table 6 presents a comparison of common features for offline and online signatures [133][134][135]. Signatures, which legally impose financial and moral liabilities, are an authentication technique that is still widely used today especially in legal documents, banking, and commercial transactions.…”

Section: Signature Verificationmentioning

confidence: 99%

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Yapıcı¹,

Tekerek²,

Topaloğlu³

2019

GMBD

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Derin öğrenme (Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine öğrenmesi yöntemidir. Özellikle son on yılda, bilgisayarlı görü, nesne tanıma, konuşma tanıma, doğal dil işleme gibi birçok araştırma alanında başarılı sonuçlar elde ederek, yapay zekanın derin uykudan uyanmasına yol açmıştır. Günümüzde, çeşitli alanlardaki birçok araştırmacı, DL yöntemlerini kullanarak alanlarında en iyi sonucu almaya çalışmaktadır. Bu tarama çalışmasında, DL modelleri ve DL ile çalışılabilecek önemli araştırma konuları hakkında bilgiler vererek araştırmacılara rehberlik etmeyi hedefliyoruz. Çalışmada Özerk Araçlar (Autonomous Vehicles), Doğal Dil İşleme (Natural Language Processing), El Yazısı Karakter Tanıma (Handwritten Character Recognition), İmza Doğrulama (Signature Verification), Ses ve Video Tanıma (Voice and Video Recognition), Tıbbi Görüntü İşleme (Medical İmage Processing), Büyük Veri (Big Data) gibi dünyanın en popüler ve en zorlu alanlarında yapılan DL çalışmalarını inceliyoruz. Ayrıca, araştırmacılara yardımcı olmak için, incelediğimiz bu alanlardaki DL ile çalışılabilecek, henüz çalışılmamış veya yeterince iyi sonuçlar elde edilememiş problemlere dikkat çekerek olası araştırma konularını listeliyoruz. Günümüzde, Derin öğrenme yöntemleri nesne tanıma alanında insanlardan daha iyi sonuçlara ulaşmıştır. DL üzerine yapılan çalışmalar dikkate alındığında, bu başarının otonom araçlar, tıbbi görüntü işleme, büyük veri analizi ve karakter tanıma gibi alanlarda da gerçekleşeceği öngörülmektedir. https://dx.Deep learning (DL) is an important machine learning field that has achieved considerable success in many research areas. In the last decade, the-state-of-the-art studies on many research areas such as computer vision, object recognition, speech recognition, and natural language processing were especially led to the awakening of the artificial intelligence from deep sleep. Nowadays, many researchers try to find solutions to many problems in various fields under the light of DL methods. In this study, it is presented important knowledge to guide about DL models and challenging topics that can be used in DL for researchers. This study investigated DL studies which are made in the most popular and challenging fields such as autonomous vehicles, natural language processing, handwritten character recognition, signature verification, voice and video recognition, medical image processing, and big data. Furthermore, this study points out the remaining challenges of these research areas that can be solved by DL, and discusses future topics to help the researchers. In the present day, Deep learning methods have reached better results than humans in object recognition. According to the literature studies on DL, It is foreseen that this success will be achieved in areas such as autonomous vehicles, medical image processing, big data analysis, and character recognition. https://dx.

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“…The hybrid approach implies using a combination of the methods described above. The authors of [23] proposed an approach for verifying signatures based on the time series (x, y), similarity coefficients, and Hotelling statistics, which reduces the amount of data required for classification. Features and the similarity coefficients associated with them create new composite features, the use of which increases the accuracy of classification.…”

Section: A Handwritten Signature Authenticationmentioning

confidence: 99%

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

Bardamova

Konev

Hodashinsky

et al. 2019

JCOMSS

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Handwritten signatures are used in authentication systems as a universal biometric identifier. Signature authenticity verification requires building and training a classifier. This paper describes a new approach to the verification of handwritten signatures by dynamic characteristics with a fuzzy rule-based classifier. It is suggested to use the metaheuristic Gravitational Search Algorithm for the selection of the relevant features and tuning fuzzy rule parameters. The efficiency of the approach was tested with an original dataset; the type II errors in finding the signature authenticity did not exceed 0.5% for the worst model and 0.08% for the best model.

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Dynamic signature verification method based on association of features with similarity measures

Cited by 41 publications

References 44 publications

Online Signature Verification for Forgery Detection

Online Signature Verification for Forgery Detection

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Gravitational Search For Designing A Fuzzy Rule-Based Classifiers For Handwritten Signature Verification

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