Grid structured morphological pattern spectrum for off-line signature verification

Shekar, B. H.; Bharathi, R.; Kittler, Josef; Vizilter, Yu. V.; Mestestskiy, Leonid

doi:10.1109/icb.2015.7139106

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…They trained multiple networks on features extracted at different resolutions, and another network to make a decision, based on the outputs of these networks. Shekar et al [59] presented a comparison of neural networks and support vector machines in three datasets.…”

Section: Neural Network and Deep Learningmentioning

confidence: 99%

Offline handwritten signature verification — Literature review

Hafemann

Sabourin

Oliveira

2017

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)

156

118

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The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

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Section: Neural Network and Deep Learningmentioning

confidence: 99%

Offline handwritten signature verification — Literature review

Hafemann

Sabourin

Oliveira

2017

2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)

156

118

View full text Add to dashboard Cite

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“…For a more detailed review of OSV see the recent review paper [1]. The most previous researches on the OSV has been dedicated to the design of hand-crafted feature extraction [1,[3][4][5]. However, In the last five years, automatic feature learning by CNNs has significantly improved the performance of OSV systems [1,2].…”

Section: A Offline Signature Verificationmentioning

confidence: 99%

Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach

Mersa

Etaati

Masoudnia

et al. 2019

2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA)

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Offline Signature Verification (OSV) is a challenging pattern recognition task, especially when it is expected to generalize well on the skilled forgeries that are not available during the training. Its challenges also include small training sample and large intra-class variations. Considering the limitations, we suggest a novel transfer learning approach from Persian handwriting domain to multi-language OSV domain. We train two Residual CNNs on the source domain separately based on two different tasks of word classification and writer identification. Since identifying a person's signature resembles identifying one's handwriting, it seems perfectly convenient to use handwriting for the feature learning phase. The learned representation on the more varied and plentiful handwriting dataset can compensate for the lack of training data in the original task, i.e. OSV, without sacrificing the generalizability. Our proposed OSV system includes two steps: learning representation and verification of the input signature. For the first step, the signature images are fed into the trained Residual CNNs. The output representations are then used to train SVMs for the verification. We test our OSV system on three different signature datasets, including MCYT (a Spanish signature dataset), UTSig (a Persian one) and GPDS-Synthetic (an artificial dataset). On UT-SIG, we achieved 9.80% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 17.45%. Our proposed method surpassed state-of-the-arts by 6% on GPDS-Synthetic, achieving 6.81%. On MCYT, EER of 3.98% was obtained which is comparable to the best previously reported results.

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“…Different learning strategies, such as machine learning and similarity-based approaches, have been proposed in the literature to solve the problem of signature verification. Machine learning approaches include Neural Networks (NNs) [4,7,19,42], Bayes classifier [3], Hidden Markov Models (HMMs) [30,34,36,38,41], Support Vector Machines (SVMs) [5, 10-14, 27-28, 31, 37, 38, 40-42], Gaussian Mixture Models (GMMs) [6], Gentle AdaBoost algorithm [39], and Ensembles of classifiers [41]. Similarity-based approaches comprise the following: k-Nearest Neighbour (kNN), Dynamic Time Warping, and point matching [3,6,8,12,19,29,[32][33][34][35][36].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Signature Verification Method Based on an Interval Symbolic Representation and a Fuzzy Similarity Measure

Alaei

Pal

et al. 2017

IEEE Trans.Inform.Forensic Secur.

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In this paper an efficient off-line signature verification method based on an interval symbolic representation and a fuzzy similarity measure is proposed. In the feature extraction step, a set of Local Binary Pattern (LBP) based features is computed from both the signature image and its under-sampled bitmap. Interval-valued symbolic data is then created for each feature in every signature class. As a result, a signature model composed of a set of interval values (corresponding to the number of features) is obtained for each individual's handwritten signature class. A novel fuzzy similarity measure is further proposed to compute the similarity between a test sample signature and the corresponding interval-valued symbolic model for the verification of the test sample. To evaluate the proposed verification approach, a benchmark offline English signature dataset (GPDS-300) and a large dataset (BHSig260) composed of Bangla and Hindi off-line signatures were used. A comparison of our results with some recent signature verification methods available in the literature was provided in terms of average error rate and we noted that the proposed method always outperforms when the number of training samples is eight or more.

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Grid structured morphological pattern spectrum for off-line signature verification

Cited by 15 publications

References 9 publications

Offline handwritten signature verification — Literature review

Offline handwritten signature verification — Literature review

Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach

An Efficient Signature Verification Method Based on an Interval Symbolic Representation and a Fuzzy Similarity Measure

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