Gornale, Shivanand, Kumar, Sathish, Siddalingappa, Rashmi ORCID: https://orcid.org/0000-0001-9786-8436 and Hiremath, Prakash S. (2022) Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques. Transactions on Machine Learning and Artificial Intelligence, 10 (2). pp. 27-60.

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Abstract

The handwritten signature is considered one of the most widely accepted personal behavioral traits in Biometric system. Handwriting analysis has wide applications in multiple domains such as psychological disorders, medical diagnosis, and recruitment of staff, career counseling, writer credentials, forensic studies, matrimonial sites, e-security, e-health and many more. In this paper, we recapitulate the state-of-the-art techniques and applications based on the handwriting signature analysis for the Assessment of Neurological Disorder using Machine Learning Techniques, In addition to this, achievements and challenges the scientific community should address. Thus, an integrated discussion of various datasets used, feature extraction techniques and classification schemes regarding Parkinson’s disease (PD) and Alzheimer’s disease (AD) is done and surveyed scientifically. The present research paper aims to provide an extensive review of scientific literature, ascertain vulnerable challenges and offer new research directions in the field.

Item Type:	Article
Status:	Published
DOI:	10.14738/tmlai.102.12210
Subjects:	Q Science > Q Science (General) Q Science > Q Science (General) > Q325 Machine learning
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/12844

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