UTSig: A Persian offline signature dataset

Soleimani, Amir; Fouladi, Kazim; Araabi, Babak Nadjar

doi:10.1049/iet-bmt.2015.0058

Cited by 61 publications

(27 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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)

View full text Add to dashboard Cite

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.

show abstract

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)

View full text Add to dashboard Cite

show abstract

“…On UT-Sig, we achieved EER of 5.32% which is substantially better than the best reported EER of 17.45% by (Soleimani et al, 2016) in the literature. Since this dataset has recently published (Soleimani et al, 2017), few studies (Soleimani et al, 2016;Soleimani et al, 2017) applied their methods on it. Therefore, we applied state-of-the-art methods, including SigNet, and SigNet-F (Hafemann et al, 2017a) and also Snapshot Ensemble to learn features on UT-Sig dataset in order for fair comparison.…”

Section: Comparison With the State-of-the-art In Different Signature mentioning

confidence: 99%

Multi-representational learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs

Masoudnia

Mersa

Araabi

et al. 2019

Expert Systems with Applications

View full text Add to dashboard Cite

Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in the presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalization for OSV by examining different loss functions for Convolutional Neural Network (CNN). We adopt our new approach to OSV by asking two questions: 1. which classification loss provides more generalization for feature learning in OSV? , and 2. How integration of different losses into a unified multi-loss function lead to an improved learning framework?These questions are studied based on analysis of three loss functions, including cross entropy, Cauchy-Schwarz divergence, and hinge loss. According to complementary features of these losses, we combine them into a dynamic multi-loss function and propose a novel ensemble framework for simultaneous use of them in CNN. Our proposed Multi-Loss Snapshot Ensemble (MLSE) consists of several sequential trials. In each trial, a dominant loss function is selected from the multi-loss set, and the remaining losses act as a regularizer. Different trials learn diverse representations for each input based on signature identification task. This multi-representation set is then employed for the verification task. An ensemble of SVMs is trained on these representations, and their decisions are finally combined according to the selection of most generalizable SVM for each user.We conducted two sets of experiments based on two different protocols of OSV, i.e., writer-dependent and writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and UT-SIG. Based on the writerdependent OSV protocol, On UT-SIG, we achieved 6.17% Equal Error Rate (EER) which showed substantial improvement over the best EER in the literature, 9.61%. Our method surpassed state-of-the-arts by 2.5% on GPDS-Synthetic, achieving 6.13%. Our result on MCYT was also comparable to the best previous results.The second set of experiments examined the robustness of our proposed method to the arrival of new users enrolled in the OSV system based on the writer-independent protocol. The results also confirmed that our proposed system efficiently performed the verification of new users enrolled in the OSV system.

show abstract

“…As a result, the files of this dataset include images having two different aspect ratios; this phe-nomenon conveys a structural distortion highlighted during the feature extraction procedure. The fourth signature dataset is the Persian UTSIG, created by Soleimani et al [50]. It contains specimens from 115 writers where each one has 27 genuine signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6 forgers.…”

Section: Datasets and Experimental Protocolsmentioning

confidence: 99%

A Comprehensive Study of Sparse Representation Techniques for Offline Signature Verification

Zois

Tsourounis

Theodorakopoulos

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

View full text Add to dashboard Cite

In this work, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS and UTSIG. Beyond the accuracy improvements, several major parameters associated with sparse representation; such as selected configuration, dictionary size, sparsity level and positivity priors are investigated. Besides, it is evinced that 2 nd order statistics of the sparse codes is a powerful pooling function for the formation of the global signature descriptor. Also, a thorough evaluation of the effects of preprocessing is introduced by an automated algorithm in order to select the optimum thinning level. Finally, a segmentation strategy which employs a special form of spatial pyramid tailored to the problem of sparse representation is presented along with the enhancing of the produced descriptor on meaningful areas of the signature as emerged from the BRISK key-point detection mechanism. The obtained state-of-the-art results on the most challenging signature datasets provide a strong indication towards the benefits of learned features, even in writer dependent (WD) scenarios with a unique model for each writer and only a few available reference samples of him/her.

show abstract

UTSig: A Persian offline signature dataset

Cited by 61 publications

References 29 publications

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

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

Multi-representational learning for Offline Signature Verification using Multi-Loss Snapshot Ensemble of CNNs

A Comprehensive Study of Sparse Representation Techniques for Offline Signature Verification

Contact Info

Product

Resources

About