An Application of GMM Algorithm in Signature Skew Detection

T. M. Rajesh*, V.N. Manjunath Aradhya**
* Research Scholar, Department of Computer Science & Engineering, Jain University, Bangalore, Karnataka, India.
** Associate Professor, Department of Master of Computer Applications, Sri Jayachamaraja College of Engineering, Mysuru, Karnataka, India.
Periodicity:September - November'2015
DOI : https://doi.org/10.26634/jpr.2.3.3757

Abstract

Signature Identification and Verification (SIV) system is one of the oldest behavioral biometrics, which is being more widely used for the identification and verification applications by a person. Handwritten signature written with a skew is a hurdle to any SIV system. If one has to achieve the accurate results in identification and verification process using signature as a biometric trait, we need to remove the skew of the signatures which are scanned from the documents, and in order to estimate the skew angle and correct the skewness of the signature, skew detection stage is the most important step to be taken care off. In this paper the authors present a Gaussian Mixture Model to estimate the skew angle of a signature. Experimentation is carried out on the Kannada signature database of 30 users.

Keywords

Signature, Pre-processing, Gaussian Mixture Model, Skew Detection, Skew Correction

How to Cite this Article?

Rajesh, T. M., and Aradhya, M. V. N. (2015). An Application of GMM in Signature Skew Detection. i-manager’s Journal on Pattern Recognition, 2(3), 8-15. https://doi.org/10.26634/jpr.2.3.3757

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