Intrapersonal Parameter Optimization for Offline Handwritten Signature Augmentation

Maruyama, Teruo Matos; Oliveira, Luiz S.; Britto, Alceu S.; Sabourin, Robert

doi:10.1109/tifs.2020.3033442

Cited by 20 publications

(11 citation statements)

References 62 publications

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“…Our model overcomes the pre-trained model. [36] 98.49% -Jampour and Naserasadi (2019) [37] 98.76% -Li Liu et al (2021) [38] -6.74 Maruyama et al (2021) [39] -0.82 Alsuhimat and Mohamad [40] 87.7% 11.40 Kadhm et al [41] 99.7% -Proposed Method 100% 0.0…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

Signature verification based on proposed fast hyper deep neural network

Hashim,

Mohsin,

Alkhayyat

2024

IJ-AI

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<span>Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearance-based features, gray-level co-occurrence matrix (GLCM) as texture-features, and fast Fourier transform (FFT) as frequency-features are extracted from signature images in order to build a hybrid feature vector for each image. Finally, to classify signature features, we have designed a proposed fast hyper deep neural network (FHDNN) architecture. Two different datasets are used to evaluate our model these are SigComp2011, and CEDAR datasets. The results collected demonstrate that the suggested model can operate with accuracy equal to 100%, outperforming several of its predecessors. In the terms of (precision, recall, and F-score) it gives a very good results for both datasets and exceeds (1.00, 0.487, and 0.655 respectively) on Sigcomp2011 dataset and (1.00, 0.507, and 0.672 respectively) on CEDAR dataset.</span>

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Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =mentioning

confidence: 99%

Signature verification based on proposed fast hyper deep neural network

Hashim,

Mohsin,

Alkhayyat

2024

IJ-AI

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“…Maruyama et al 26 designed an intrapersonal parameter optimization method for offline handwritten signature augmentation and used in an automatic signature verification system (ASVS). The goal of the ASVS is to increase the accuracy of signature sample and predicts the writer’s variability and features.…”

Section: Related Workmentioning

confidence: 99%

Handwriting identification and verification using artificial intelligence-assisted textural features

Zhao,

2023

Sci Rep

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Intelligent process control and automation systems require verification authentication through digital or handwritten signatures. Digital copies of handwritten signatures have different pixel intensities and spatial variations due to the factors of the surface, writing object, etc. On the verge of this fluctuating drawback for control systems, this manuscript introduces a Spatial Variation-dependent Verification (SVV) scheme using textural features (TF). The handwritten and digital signatures are first verified for their pixel intensities for identification point detection. This identification point varies with the signature’s pattern, region, and texture. The identified point is spatially mapped with the digital signature for verifying the textural feature matching. The textural features are extracted between two successive identification points to prevent cumulative false positives. A convolution neural network aids this process for layered analysis. The first layer is responsible for generating new identification points, and the second layer is responsible for selecting the maximum matching feature for varying intensity. This is non-recurrent for the different textures exhibited as the false factor cuts down the iterated verification. Therefore, the maximum matching features are used for verifying the signatures without high false positives. The proposed scheme’s performance is verified using accuracy, precision, texture detection, false positives, and verification time.

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“…signal vs. time) [5][6][7] or static-offline (i.e. image) [8][9][10][11][12][13]. An alternative classification of offline signature verification methodologies divides them into a) handcrafted methods, which mainly utilize image processing and computer vision techniques and b) data-driven or learningbased approaches with typical representatives Bags of Visual Words [14,15] sparse representation [11] and deep learning methodologies [8,12,[16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…A number of deep learning based topologies are obtained by examining different loss functions [31] and similarity strategies [8,10,30] commonly used on the dominant SigNet architecture. Also, post feature management methods are applied, exploiting the effectiveness of the extracted vectors [13,33].…”

Section: Introductionmentioning

confidence: 99%

“…In the WD approach a dedicated classifier is trained for each signatory with his/her reference samples [36][37]. They can perform efficiently with few reference samples, especially when synthetic signature samples are used [13,38,39]. The WI approach usually learns one global classifier which attempts to model the probability distribution between a) the intra-class of genuine-genuine pairs of signatures and b) the inter-class of genuine-forgery pairs by forming a (dis)similarity distance space [33,35,40,41].…”

Section: Introductionmentioning

confidence: 99%

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