Multi-expert verification of hand-written signatures

Bovino, L.; Impedovo, S.; Pirlo, Giuseppe; Sarcinella, L.

doi:10.1109/icdar.2003.1227796

Cited by 24 publications

(19 citation statements)

References 12 publications

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“…For instance, string matching [31], [349] is used not only for signature verification but also for signature identification purposes, via advanced local associative indexing [118]. In other cases, the structural description graph is used to verify the structural organization of a questioned signature [18], [56], [129], as Fig. 7 illustrates.…”

Section: Classificationmentioning

confidence: 99%

“…Several decision combination schemes have been implemented, ranging from majority voting [4], [50], [51], [59], [274] to Borda count [12], from simple and weighted averaging [18] to Dempster-Shafer evidence theory [12] and NNs [15], [17], [26]. The boosting algorithm has been used to train and integrate different classifiers, for both verification of online [122] and offline [329] signatures.…”

Section: Classificationmentioning

confidence: 99%

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Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

538

295

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Abstract-In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

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Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Automatic Signature Verification: The State of the Art

Impedovo

Pirlo

2008

IEEE Trans. Syst., Man, Cybern. C

538

295

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“…It defines an edit distance between strings, by eliminating the unrealistic constraint of equal lengths. This algorithm is widespread and has already been used in several applications: handwriting recognition [27], speech recognition [34], and signature verification [35].…”

Section: Edit Distancementioning

confidence: 99%

Writer verification based on a single handwriting word samples

Paquet

2016

J Image Video Proc.

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The writer recognition task has received a lot of interests during the last decade due to it wide range of applications. This task includes writer identification and/or writer verification. However, all the researches assumed that they dispose of a large amount of text to identify or authenticate the writer, which is never the case in real-life applications. In this paper, we present an original approach for the writer authentication task based on the analysis of a unique sample of a handwriting word. We used the Levenshtein edit distance based on Fisher-Wagner algorithm to estimate the cost of transforming one handwritten word into another. Such method has been successfully applied for signature authentication and voice recognition. In order to apply it to handwriting words, we developed a segmentation module to generate the graphemes; considered as elementary components for each word. We evaluated this approach on part of the IAM database (100 writers), where half of them provided three samples only of the same word. The obtained results are very promising since we succeed to accept correctly in 87 % of cases when we used the whole database (100 writers) and up to 92 % when we used 40 writers.

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“…times j th classifier selected for i th writer Number of trials conducted (6) The classifier having the highest frequency say C i j shall be the best classifier for the i th writer and is selected for writer i . Similarly for all writers in the database, a classifier is selected using the above mentioned procedure.…”

Section: Classifier Selectionmentioning

confidence: 99%

“…Verification is done in stages considering these categories of features and a test signature is accepted as genuine if it passes through all the three stages. In multi expert approach [6] , a signature is segmented into different strokes and each stroke is represented in different domains. Each stroke is authenticated individually and the final decision is taken based on the weighted average of the decisions of individual strokes.…”

Section: Introductionmentioning

confidence: 99%

Online signature verification based on writer dependent features and classifiers

Manjunatha

Manjunath

Guru

et al. 2016

Pattern Recognition Letters

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a b s t r a c tIn this work, an approach for online signature verification based on writer specific features and classifier is investigated. Existing models for online signatures are generally writer independent, as a common classifier or fusion of classifier is used on a common set of features for all writers during verification. In contrast, our approach is based on the usage writer dependent features as well as writer dependent classifier. The two decisions namely optimal features suitable for a writer and a classifier to be used for authenticating the writer are taken based on the error rate achieved with the training samples. The performance of our model is tested on both MCYT-100 (DB1), a sub corpus of MCYT data set, consisting of signatures of 100 writers, MCYT-330 (DB2) consisting of signatures of all 330 writers and visual subcorpus of SUSIG dataset. Experimental results confirm the effectiveness of writer dependent characteristics for online signature verification. The error rate that we achieved is lower when compared to many existing contemporary works on online signature verification especially when the number of training samples available for each writer is sufficient enough.

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Multi-expert verification of hand-written signatures

Cited by 24 publications

References 12 publications

Automatic Signature Verification: The State of the Art

Automatic Signature Verification: The State of the Art

Writer verification based on a single handwriting word samples

Online signature verification based on writer dependent features and classifiers

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