Static Signature Synthesis: A Neuromotor Inspired Approach for Biometrics

Ferrer, Miguel A.; Diaz, Moises; Morales, Aythami

doi:10.1109/tpami.2014.2343981

Cited by 103 publications

(62 citation statements)

References 34 publications

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“…The present research work is related to a number of different areas within signature biometrics such as on-line and off-line monomodal signature verification [4,5] or synthetic handwritten signature generation [12,10,13]. Each of these fields presents a solid research background with multiple studies impossible to cover here extensively.…”

Section: Related Workmentioning

confidence: 99%

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

Galbally

Diaz

Ferrer

et al. 2015

Pattern Recognition

Self Cite

107

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This is the author’s version of a work that was accepted for publication in Pattern Recognition . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition , 48, 9 (2005) DOI: 10.1016/j.patcog.2015.03.019On-line signature verification still remains a challenging task within biometrics. Due to their behavioral nature (opposed to anatomic biometric traits), signatures present a notable variability even between successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exact same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios.M. D.-C. is supported by a PhD fellowship from the ULPGC and M.G.-B. is supported by a FPU fellowship from the Spanish MECD. This work has been partially supported by projects: MCINN TEC2012-38630- C04-02, Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM-Telefónic

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Section: Related Workmentioning

confidence: 99%

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

Galbally

Diaz

Ferrer

et al. 2015

Pattern Recognition

Self Cite

107

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“…A Persian handwriting dataset was used as the source domain. The signature datasets as the target domain in this study are MCYT-75 [19], UTSig [20] and GPDS-synthetic [21]. A set of samples from UTSig dataset are shown in TABLE IV.…”

Section: Resultsmentioning

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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“…In their work, they considered a set of "slight distortions", used to create new genuine samples, and "heavy distortions" to generate forgeries from the genuine samples. More recently, Ferrer et al [19], [18], [11] have proposed a signature synthesis approach inspired on a neuromotor model.…”

Section: E Data Augmentationmentioning

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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Static Signature Synthesis: A Neuromotor Inspired Approach for Biometrics

Cited by 103 publications

References 34 publications

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

On-line signature recognition through the combination of real dynamic data and synthetically generated static data

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

Offline handwritten signature verification — Literature review

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