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.
Cited by
3 articles.
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1. Offline Handwritten Signature Analysis for Age Classification using Deep Features;2023 4th International Conference for Emerging Technology (INCET);2023-05-26
2. Gender Classification from Behavioural Biometric Data using Convolutional Neural Network;Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022);2023
3. Advantages and Functions of Clinical and Decision Support Systems;Journal of Biomedical and Sustainable Healthcare Applications;2022-01-05