Offline signature identification using the histogram of symbolic representation

Djoudjai, Mohamed Anis; Chibani, Youcef; Abbas, Nassim

doi:10.1109/icee-b.2017.8192092

Cited by 5 publications

(8 citation statements)

References 20 publications

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“…With the same setup, Djoudjai's et al . approach [5] achieved

98.63 %

accuracy where they used HOG features and histogram of symbolic representation (HSR) classifier for identification while we achieved 97.43% accuracy with our proposed approach on the same setup.…”

Section: Resultsmentioning

confidence: 86%

“…Hadjadji et al [3] reported 92.05, 97.51, and 97.99% accuracy using Curvelet Transform with equi-spaced (ES), equi-mass (EM) and choquet fuzzy integral, respectively. With the same setup, Djoudjai's et al approach [5] achieved 98.63% accuracy where they used HOG features and histogram of symbolic representation (HSR) classifier for identification while we achieved 97.43% accuracy with our proposed approach on the same setup.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 79%

“…We used the same setup as described in [3, 5] for handwritten signature identification where they used only the genuine signatures for evaluation. The proposed protocol relies on employing five signature instances for training and 19 remaining signatures used for test.…”

Section: Resultsmentioning

confidence: 99%

“…Signature authentication can be divided into two categories: Signature identification (i.e. 1:N, one-to-many) [3][4][5] and signature verification (i.e. 1:1, matching) [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Chaos game theory and its application for offline signature identification

Jampour

Naserasadi²

2019

IET biom.

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Chaos game mechanism is a procedure for creating fractal phenomena from another fractal using a polygon and random walk. Here, the authors propose a novel identification approach and efficient application of Chaos Game theory towards signature identification. In fact, a signature is produced with hand scripting with a special rhythm that belongs to a specific person. Although a signature is a behavioural feature among the human biometric features, this behavioural feature expresses a chaotic property and can be analysed with chaotic systems and fractal theory. With authors' technique, by using a Chaos Game theory, a new fractal is created for each signature instance and, during the creation of the fractal, new features are extracted. These features express the fractal properties of the signature and are unique. In addition, by using fractal theory, this technique benefits from the advantages of fractal phenomena such as stability against rotation, losing some parts of the signature and scale that is desirable for biometrics applications. Authors' approach for offline signature analysis can segregate and identify many signature instances with a desirable time complexity. The authors name the technique that the authors present here the chaos game signature identification (CGSI).

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“…With the same setup, Djoudjai's et al . approach [5] achieved

98.63 %

Section: Resultsmentioning

confidence: 86%

Section: Comparison With the State-of-the-artmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 99%

“…Signature authentication can be divided into two categories: Signature identification (i.e. 1:N, one-to-many) [3][4][5] and signature verification (i.e. 1:1, matching) [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Chaos game theory and its application for offline signature identification

Jampour

Naserasadi²

2019

IET biom.

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“…Offline Signature identification is considered more difficult than online signature since offline signature does not have a dynamic feature that is present on online signature [1]. Offline signatures depend only on the capture signature shape available from the signature image, while online signatures can use various features such as pressure points and velocity of the drawn signature [3].…”

Section: Introductionmentioning

confidence: 99%

Offline Signature Identification Using Deep Learning and Euclidean Distance

Nugraha

Nurhadiyatna²,

Arsa

2021

LKJITI

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Hand signature is one of human characteristic that human have since birth, which can be used as identity recognition. A high accuracy signature recognition is needed to identify the right owner of signature. This study present signature identification using a combination method between Deep Learning and Euclidean Distance. 3 different signature datasets are used in this study which consist of SigComp2009, SigComp2011, and private dataset. Signature images preprocessed using binary image conversion, Region of Interest, and thinning. Several testing scenarios is applied to measure proposed method robustness, such as usage of various Pretrained Deep Learning, dataset augmentation, and dataset split ratio modifier. The best accuracy achieved is 99.44% with high precision rate.

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Interval Versus Histogram of Symbolic Representation Based One-Class Classifier for Offline Handwritten Signature Verification

Djoudjai

Chibani

2022

Artificial Intelligence and Its Applications

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Offline signature identification using the histogram of symbolic representation

Cited by 5 publications

References 20 publications

Chaos game theory and its application for offline signature identification

Chaos game theory and its application for offline signature identification

Offline Signature Identification Using Deep Learning and Euclidean Distance

Interval Versus Histogram of Symbolic Representation Based One-Class Classifier for Offline Handwritten Signature Verification

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