Learning Approach for Offline Signature Verification Using Vector Quantization Technique

Chugh, Aarti; Jain, Charu; Singh, Priti; Rana, Preeti

doi:10.1007/978-3-319-13728-5_38

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…This system as modern state of the art ASR system can act for pre -processing or feature extraction as w ell as acoustic, lexical, and language models. Some procedures have been developed for acoustic modeling, including Dynamic Time Warping (DTW), Hidden Markov Model (HMM), Vector Quantization, and Neural N etworks [48]. HMM is a model based on the dominant recognition paradigm, in which speech changes are statistically modeled.…”

Section: Multi-model Asr Systemmentioning

confidence: 99%

An Integrated Model for Evaluation of Big Data Interactive Systems Under Uncertainty

Asemi

2021

Preprint

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The study aimed to propose a judgment-based evaluation model for usability evaluating of big data interactive system s. Human judgment is associated with uncertainty and gray information. We used the fuzzy technique for integration, summarization, and distance calculation of quality value judgment. The proposed model is an integrated fuzzy Multi Factors Evaluation (MFE) model based on experts’ judgments in HCI, ISPD, and AMLMs. We provided a Fuzzy Inference System (FIS) for scoring usability evaluation metrics in different big data interactive system s. A multi-model big data interactive system is implemented for experimental testing of the model. The achieved results from the proposed model and experimental tests are compared using statistical correlation tests. The results show the ability of the proposed model for usability evaluation of big data interactive systems without the need for conducting empirical tests. It is concluded that applying a dataset in a neuro-FIS and training system cause to produce more than a hundred effective rules. The findings indicate that the proposed model can be applied for big data interactive system evaluation, informative evaluation, and complex empirical tests. Future studies may improve the FIS with the integration of artificial neural networks.

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Section: Multi-model Asr Systemmentioning

confidence: 99%

An Integrated Model for Evaluation of Big Data Interactive Systems Under Uncertainty

Asemi

2021

Preprint

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Two strategies to optimize the decisions in signature verification with the presence of spoofing attacks

et al. 2016

Information Sciences

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Fuzzy Logic Based Adaptive Resonance Theory-1 Approach for Offline Signature Verification

Jain¹,

Singh

Rana

2017

Image Processing &Amp; Communications

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This paper presents the use fuzzy logic with adaptive resonance theory-1 in signature verification. Fuzzy model is capable of stable learning of recognition categories in response to arbitrary sequences of binary input pattern. The work was carried out on two famous available signature corpuses i.e. MCYT (Online Spanish signatures database) and GPDS (Grupo de Procesado Digital de la se?al). Local binary patterns (LBP) and Gray Level Co-occurrence Matrices (GLCM) features were calculated for robust offline signature verification system. Training and verification was done using fuzzy adaptive resonance theory-1(FART-1). The system is trained and verified for different datasets to increase the accuracy of the classifier. The results thus obtained are robust than other existing techniques. The FAR and FRR for the system is 0.74% and 0.83% respectively.

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Learning Approach for Offline Signature Verification Using Vector Quantization Technique

Cited by 4 publications

References 20 publications

An Integrated Model for Evaluation of Big Data Interactive Systems Under Uncertainty

An Integrated Model for Evaluation of Big Data Interactive Systems Under Uncertainty

Two strategies to optimize the decisions in signature verification with the presence of spoofing attacks

Fuzzy Logic Based Adaptive Resonance Theory-1 Approach for Offline Signature Verification

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