Online signature verification using hybrid wavelet transform

Chavan, Manoj; Singh, Ravish R.; Bharadi, Vinayak Ashok

doi:10.11591/ijece.v10i2.pp1823-1832

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Testing has been carried out for 40 users and then the average FRR and FAR are calculated. [23][24] [25] Performance analysis for various combinations of HWT-2 for Left-Right model are shown in the table below. We have kept the number of training samples same, as 10 and number of symbols to be 275, for all trials.…”

Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Manoj Chavan

2024

jes

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Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verification and forgery detection. This feature vector is given to the Left-Right and Ergodic modelled HMM classifier for analysis. HMM is trained using 10 randomly chosen genuine signature samples and is used to test remaining 10 genuine signatures and 20 forged signatures of 40 users of SVC 2004 signature database. This process is iterated 20 times and then average values are calculated. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Left Right HMM model, DCT 4 Haar 64 HWT-1 offers best performance of FRR, FAR of 1.05%, 0.99% respectively for state 3. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Ergodic HMM model, DCT 4 DHT 64 offers best performance of FRR, FAR of 1.10%, 2.88% respectively for state 5. We conclude that comparing HWT-1 and HWT-2 combinations for Left Right and Ergodic HMM model, HWT-1 for Left Right model offer better performance.

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Section: Resultsmentioning

confidence: 99%

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Manoj Chavan

2024

jes

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“…[ 30 ] The grayscale spectrograms created by wavelet transforms have been used to train support vector machine (SVM) network. In the study by Chang et al ,[ 31 ] five time signals including horizontal and vertical position, pressure, pen azimuth, and altitude have been considered for each signature. Then, different wavelets such as Haar, Daubechies, Symlet, and Coiﬂet have been used to decompose the time signal up to five-level decomposition.…”

Section: Methodsmentioning

confidence: 99%

Online handwritten signature verification and recognition based on dual-tree complex wavelet packet transform

2020

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“…Many approaches have been suggested in the literature for approximating the time functions accompanying the signing procedure. The Fourier Transform was applied in [17], whereas the Wavelet Transform was introduced in [26,27]. In [6,12] Legendre Orthogonal Polynomials were used.…”

Section: Previous Workmentioning

confidence: 99%

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

Hefny¹,

Hassanien²,

Basha³

2021

Computers, Materials &Amp; Continua

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Identity verification using authenticity evaluation of handwritten signatures is an important issue. There have been several approaches for the verification of signatures using dynamics of the signing process. Most of these approaches extract only global characteristics. With the aim of capturing both dynamic global and local features, this paper introduces a novel model for verifying handwritten dynamic signatures using neutrosophic rule-based verification system (NRVS) and Genetic NRVS (GNRVS) models. The neutrosophic Logic is structured to reflect multiple types of knowledge and relations among all features using three values: truth, indeterminacy, and falsity. These three values are determined by neutrosophic membership functions. The proposed model also is able to deal with all features without the need to select from them. In the GNRVS model, the neutrosophic rules are automatically chosen by Genetic Algorithms. The performance of the proposed system is tested on the MCYT-Signature-100 dataset. In terms of the accuracy, average error rate, false acceptance rate, and false rejection rate, the experimental results indicate that the proposed model has a significant advantage compared to different well-known models.

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Online signature verification using hybrid wavelet transform

Cited by 4 publications

References 16 publications

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Online handwritten signature verification and recognition based on dual-tree complex wavelet packet transform

Neutrosophic Rule-Based Identity Verification System Based on Handwritten Dynamic Signature Analysis

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