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Journal ArticleDOI

A new online signature verification system based on combining Mellin transform, MFCC and neural network

Asghar Fallah, +2 more
- 01 Mar 2011 - 
- Vol. 21, Iss: 2, pp 404-416
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TLDR
A new online signature verification system based on Mellin transform in combination with an MFCC is presented and Experimental result indicates that the combination proposed method with neural network have better performance.
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This article is published in Digital Signal Processing.The article was published on 2011-03-01. It has received 45 citations till now. The article focuses on the topics: Mellin transform & Linear classifier.

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Citations
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Journal ArticleDOI

Deep Learning for Biometrics: A Survey

TL;DR: This article surveys 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities and discusses how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.
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Online Signature Verification on Mobile Devices

TL;DR: The results show that the performance of the proposed technique is comparable and often superior to state-of-the-art algorithms despite its simplicity and efficiency.
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Signatures verification based on PNN classifier optimised by PSO algorithm

TL;DR: The results of the study carried on signatures of the SVC2004 and MCYT databases demonstrate the effectiveness of the proposed approach in comparison with other methods from the literature.
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Online signature verification based on signatures turning angle representation using longest common subsequence matching

TL;DR: Experimental results using varying TAS(S) representation parameters on two publicly available signature databases show the improved performance of the selected feature along with the chosen elastic distance measure on the equal error rate results of the online signature verification task.
Journal ArticleDOI

Discriminative feature selection for on-line signature verification

TL;DR: Two methods, which are based on full factorial experiment design and optimal orthogonal experiment design, are proposed for selecting discriminative features among candidates to improve the robustness of on-line handwritten signatures.
References
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Journal ArticleDOI

Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences

TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.
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An introduction to biometric recognition

TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
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Automatic signature verification and writer identification — the state of the art

TL;DR: A survey of the literature on automatic signature verification and writer identification by computer, and an overview of achievements in static and dynamic approaches to solving these problems, with a special focus on preprocessing techniques, feature extraction methods, comparison processes and performance evaluation.
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Links between Markov models and multilayer perceptrons

TL;DR: It is shown theoretically and experimentally that the outputs of the MLP approximate the probability distribution over output classes conditioned on the input, i.e. the maximum a posteriori probabilities.
Journal ArticleDOI

Identity authentication using improved online signature verification method

TL;DR: This work presents a system for online handwritten signature verification, approaching the problem as a two-class pattern recognition problem, and received the first place at SVC2004 with a 2.8% error rate.
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