A Survey of State of the Art Methods Employed in the Offline Signature Verification Process

Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Riesen, Kaspar

doi:10.1007/978-3-030-48332-6_2

Cited by 18 publications

(9 citation statements)

References 37 publications

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“…In this section, we focus on the works related to in-air signature verification methods. We refer the reader to [19]- [23] for a detailed literature review on off-line and on-line signature verification. The first vision-based verification system that enabled capturing free hand movements in the air was proposed by Katagiri et al [24].…”

Section: Related Workmentioning

confidence: 99%

DeepAirSig: End-to-End Deep Learning Based in-Air Signature Verification

et al. 2020

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In-air signature verification is vital for biometric user identification in contact-less mode. The state-of-the-art methods use heuristics for signature acquisition, and provide insufficient data to train neural networks for the verification. In this paper, we present a novel method for end-to-end deep learning based in-air signature verification using a depth sensor. In this regard, we propose a new medium-scale in-air signature dataset which is created using an accurate convolutional neural network (CNN) based 3D hand pose estimation algorithm. The proposed dataset offers a total of 1800 signatures collected from 40 subjects. So far, dynamic time warping (DTW) has been the most effective and commonly used method for verification. Keeping in view the significant advancement in deep learning, we present a more accurate deep learning based alternative which outperforms DTW by 67.6%. To this end, we train a personalized autoencoder to reconstruct the signature of each subject. Thereafter, the signature is verified by thresholding the reconstruction loss. We perform extensive experiments by formulating spatial and depth features of the signature in images and point clouds based representations. Moreover, for comparisons, we implement several deep learning algorithms (i.e. linear autoencoder, convolutional autoencoder, and Deep One-Class classifier). Our verification approach achieves an EER of 0.055%. The dataset is available at https://bit.ly/2mEJzOw.

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Section: Related Workmentioning

confidence: 99%

DeepAirSig: End-to-End Deep Learning Based in-Air Signature Verification

et al. 2020

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“…Global features describe the global traits of the entire image. In local feature extraction, the signature image is separated into several units or parts, and features are extracted from each segment of the image [15]. Statistical features might be represented in shape, geometric data, grid, textural data, wavelet transform, or chord moments.…”

Section: Related Workmentioning

confidence: 99%

“…Spurious noise in a scanned signature image must be removed to avoid errors and make the signatures suitable for feature extraction in both the training and testing phases. However, achieving a balance between noise reduction and data loss is a challenge because some critical information is lost during preprocessing, possibly harming the system's performance in later phases [15]. The following processes are used to provide effective preprocessing of the signature image without decreasing model performance.…”

Section: Preprocessing Phasementioning

confidence: 99%

One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

Abdulhussien¹,

Nasrudin²,

Darwish³

et al. 2023

Computers, Materials &Amp; Continua

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Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection. It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages, including noninvasiveness, user-friendliness, and social and legal acceptability. According to the literature, extensive research has been conducted on signature verification systems in a variety of languages, including English, Hindi, Bangla, and Chinese. However, the Arabic Offline Signature Verification (OSV) system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes, diacritics, ligatures, and overlapping, making verification more difficult. Recently, signature verification systems have shown promising results for recognizing signatures that are genuine or forgeries; however, performance on skilled forgery detection is still unsatisfactory. Most existing methods require many learning samples to improve verification accuracy, which is a major drawback because the number of available signature samples is often limited in the practical application of signature verification systems. This study addresses these issues by presenting an OSV system based on multifeature fusion and discriminant feature selection using a genetic algorithm (GA). In contrast to existing methods, which use multiclass learning approaches, this study uses a oneclass learning strategy to address imbalanced signature data in the practical application of a signature verification system. The proposed approach is tested on three signature databases (SID-Arabic handwriting signatures, CEDAR (Center of Excellence for Document Analysis and Recognition), and UTSIG (University of Tehran Persian Signature), and experimental results show that the proposed system outperforms existing systems in terms of reducing the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (ERR). The proposed system achieved 5% improvement.

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“…Despite the technological progress, handwriting signature is still the best way to validate official documents like diplomas, bank checks, medical prescriptions, etc. Signature verification has the main difficulty of data collection [1]. In fact, only a small number of real signatures can be obtained in real life scenarios, and therefore the verification system fails to generalize.…”

Section: Introductionmentioning

confidence: 99%

Temporal Residual Networks basedbeta-elliptic model and multi-head attentionfor online handwriting signature verification

Zouari¹,

Abbasi²,

Boubaker³

et al. 2023

Preprint

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In this work, we proposed a new system for online handwriting sig-nature verification based on beta elliptic modeling and TemporalResidual Neural Networks based Multi-Head Attention model. Thebeta-elliptic modeling was applied to divide the handwriting signaturetrajectory into strokes by inspecting the extremum velocity instantsand extract their dynamic and geometric proprieties. In the verifica-tion process, residual networks based temporal convolution was devel-oped to deals with sequential input data. Furthermore, we appliedMulti head attention model to focus on a few particular aspectsat a time and carry out an efficient and sequential data process-ing . The experiments were done on two public databases SVC-2004and SCUT-MMSIG, and we achieved the state-of-the-art performanceswith an Equal Error Rates equals to 0.114 and 0. 133 respectively.

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A Survey of State of the Art Methods Employed in the Offline Signature Verification Process

Cited by 18 publications

References 37 publications

DeepAirSig: End-to-End Deep Learning Based in-Air Signature Verification

DeepAirSig: End-to-End Deep Learning Based in-Air Signature Verification

One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

Temporal Residual Networks basedbeta-elliptic model and multi-head attentionfor online handwriting signature verification

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