Hybrid Wavelets based Feature Vector Generation from Multidimensional Data set for On-line Handwritten Signature Recognition

Bharadi, Vinayak Ashok; Singh, Vikas; Nemade, Bhushan

doi:10.1109/confluence.2014.6949038

Cited by 10 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the feature vector based on azimuth and altitude demonstrates superior performance compared to the one based on the Signature Pressure Map. [9] H. B. Kekre, Tanuja Sarode, and Rachana Dhannawat applied hybrid wavelets derived from Discrete Cosine Transform (DCT), Hadamard, and Kekre transforms for image fusion, yielding superior outcomes compared to conventional methods. An advantage of this technique is its versatility, as it can be employed for images of varying sizes.…”

Section: Reviewmentioning

confidence: 99%

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

Manoj Chavan

2024

jes

View full text Add to dashboard Cite

Online signature verification stands out as a distinctive biometric feature, offering both static and dynamic attributes within 2D signature images. A Hybrid Wavelet Transform-2 (HWT-2) with a size of 256 is constructed by employing the Kronecker product of two orthogonal transforms: DCT, DHT, Haar, Hadamard, and Kekre, each with sizes of 4 and 64. The HWT enables the analysis of signals at both global and local levels, akin to wavelet transforms. HWT-2 is applied to 256 samples of online handwritten signatures, and the first 128 samples of the output are utilized as feature vectors for the verification and forgery detection of online handwritten signatures. These feature vectors are inputted into Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for analysis. The HMMs are trained using 10 randomly selected genuine signature samples, and subsequently tested on the remaining 10 genuine signatures and 20 forged signatures from 40 users of the SVC 2004 signature database. This process is repeated 20 times, and the average values are computed. Among all possible combinations of HWT-2 using DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and DHT 64 demonstrates the best performance, with False Rejection Rate (FRR) and False Acceptance Rate (FAR) values of 3.96% and 1.48%, respectively, for state 5. Similarly, for the Ergodic HMM model, the combination of DCT 4 and DHT 64 exhibits the best performance, with FRR and FAR values of 1.10% and 2.88%, respectively, for state 5. These results indicate that combinations of HWT-2 outperform individual orthogonal transforms, and further, that HWT-2 combinations within the Ergodic HMM model offer superior performance compared to the Left-Right HMM model.

show abstract

Section: Reviewmentioning

confidence: 99%

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

Manoj Chavan

2024

jes

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Various classifiers based on KNN, SVM and NN [13,14] have been used for verification of signatures. In [15], KNN classifier was used with, HWTs of the pressure map of online signatures as feature vector. It offered an EER of 30%.…”

Section: … … … ]mentioning

confidence: 99%

Online signature verification using hybrid wavelet transform

Chavan¹,

Singh²,

Bharadi³

2020

IJECE

View full text Add to dashboard Cite

Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vector is given to Left to Right HMM classifier to identify the genuine and forged signature. For HWT-1, DCT HAAR offers best FAR and FRR. . For HWT-2, KEKRE 128 offers best FAR and FRR. HWT-1 offers better performance than HWT- 2 in terms of FAR and FRR. As the number of states increase, the performance of the system improves. For HWT - 1, KEKRE 128 offers best performance at 275 symbols whereas for HWT - 2, best performance is at 475 symbols by KEKRE 128.

show abstract

Hybrid Wavelets based Feature Vector Generation from Multidimensional Data set for On-line Handwritten Signature Recognition

Cited by 10 publications

References 20 publications

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

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

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

Online signature verification using hybrid wavelet transform

Contact Info

Product

Resources

About