Handwritten Signature Biometric Data Analysis for Personality Prediction System Using Machine Learning Techniques

Authors

Keywords:

Biometrics, Graphology, LDP, LPQ, Offline Handwritten Signature, Personality Analysis, Random Forest

Abstract

Handwritten signature has been considered as one of the most widely accepted behavioral personal trait in Biometric security system; and  It contains various dynamic and innate behavioral traits like shapes and patterns which can certainly find a person’s soft characteristics like age, gender, Personality, handedness and many more. Person’s signature or handwriting determines the state of the person’s mind or personality characteristics at the time of writing. This paper provides a personality prediction system of different characteristics determining the personality of a person based on offline handwritten signature Images. Experiments are carried out using supervised learning techniques. Results shows a significant recognition rate and validates the effectiveness against the state-of-art techniques in comparison to similar works.

Author Biographies

Shivanand S. Gornale, Professor

Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India

Sathish Kumar, Research Scholar

Department of Computer Science, School of Mathematics and Computing Sciences, Rani channamma Univerisity, Belagavi, Karnataka, India

Prakash S. Hiremath

Senior Professor, Department of MCA, KLE Technological University, Hubballi, Karnataka,India

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Published

2021-09-25

How to Cite

Gornale, S. S., Kumar, S., & Hiremath, P. S. . (2021). Handwritten Signature Biometric Data Analysis for Personality Prediction System Using Machine Learning Techniques. Transactions on Machine Learning and Artificial Intelligence, 9(5), 1–22. Retrieved from https://journals.scholarpublishing.org/index.php/TMLAI/article/view/10808