• Corpus ID: 86584904

Static Signature Authentication based on J48 and Random Forest

@article{Singh2017StaticSA,
  title={Static Signature Authentication based on J48 and Random Forest},
  author={Ranjan Kumar Singh and Sushila Maheshkar and Vikas Maheshkar},
  journal={International journal of engineering research and technology},
  year={2017},
  volume={6}
}
With the exposure and development of technology, a better security system is needed to protect the data. Off-line signature verification is one of the prime area of research which is most widely recommended by the research community for security issues. For the Off-line signature verification from spontaneous handwritten signature image a precise and effective method is proposed which contributes significantly to that area and comprises of image prepossessing, feature extraction and decision… 

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References

SHOWING 1-10 OF 36 REFERENCES

Handwritten Signature Verification using Neural Network

The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification, which includes applying the extracted features of test signature to a trained neural network which will classify it as a genuine or forged.

Offline Signature Cognition and Verification Using Artificial Neural Network

This project will implement offline signature verification technique using Artificial Neural Network (ANN) approach to verify the genuine person's identity.

An Offline Signature Verification using Adaptive Resonance Theory 1(ART1)

This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection using Adaptive Resonance Theory-1 (ART 1) and achieves a classification ratio of 97.9%.

Enhanced Offline Signature Recognition Using Neural Network and MDA

Off-line signature recognition & verification using neural network and MDA is proposed where the signature is captured and presented to the user in an image format therefore the signatures are verified based on parameters extracted from the signature using various image processing techniques.

Offline Handwritten Signature Verification using Neural Network

In this paper signature verification is done by means of image processing, geometric feature extraction and by using neural network technique.

An Application of the 2D Gaussian Filter for Enhancing Feature Extraction in Off-line Signature Verification

The experimental results for signature verification indicated that, by applying a suitable 2D Gaussian filter on the matrices containing the chain code histograms, an average error rate (AER) of 13.90% can be obtained whilst maintaining the false acceptance rate (FAR) for random forgeries as low as 0.02%.

Offline geometric parameters for automatic signature verification using fixed-point arithmetic

This paper presents a set of geometric signature features for offline automatic signature verification based on the description of the signature envelope and the interior stroke distribution in polar

Offline Signature Verification: An Approach Based on Score Level Fusion

A new approach for offline signature verification based on score level fusion of distance and orientation features of centroids of offline signatures using bi-interval valued symbolic feature vector is presented.

On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey

The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.

An introduction to biometric recognition

A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.