Gender Classification Based on Online Signature Features using Machine Learning Techniques

Authors

Keywords:

Biometric Data Analysis, Gender Classification, Online Handwritten Signature, Feed Forward Deep Neural Network

Abstract

A human signature gives a lot of insights into an individual’s characteristics such as illness, professional choices, and mood. From the biometric perspective, a Handwritten Signature is a behavioral trait and Gender is a demographic category (soft trait) of the person. Gender classification from handwritten signatures has been implied in several applications such as psychology and forensics. Male writings with a high intra-class variation tend to have a feminist aesthetic aspect, and vice versa. This gives clues to recognize the gender of the person using a handwritten signature. The proposed methodology is based on extracting numeric features from the male and female dynamic signature samples. Data was collected from 535 individuals of different age groups (18-65). Further, these signature samples were converted to numeric attributes resulting in 66 signature features from each data. Experiments were carried out using six different Machine Learning techniques; On the whole, the overall accuracy of these methods is 81.2% (KNN), 81.9% (LR), 77.1% and 49.3% (for both Poly and RBF kernels in SVM, respectively), Poly kernel using cross-validation resulted in 81.8% in SVM, 89.3% (DT), 96.2% (RF) and 98.2% (DL). Overall, the deep neural networks outperformed other models, immediately followed by RF.

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Author Biographies

Sathish Kumar, Research Scholar

Research Scholar, Department of Computer Science, Rani Channamma University, Belagavi-591156, INDIA

ORCID ID:  0000-0001-9374-1980

Shivanand S. Gornale, Professor

Professor, Department of Computer Science, Rani Channamma University, Belagavi-591156, INDIA

ORCID ID:  0000-0001-5373-4049

Rashmi Siddalingappa, National Post-Doctoral Fellow

National Post-Doctoral  Fellow,  Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru 560012, INDIA

ORCID ID: 0000-0001-9786-8436

Arjun Mane, Assistant Professor

Assistant Professor, Department of Digital and Cyber Forensics, Govt. Institute of Forensic Science, Nagpur-INDIA

ORCID ID :  0000-0003-4129-1863

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Published

27.05.2022

How to Cite

[1]
S. Kumar, S. S. Gornale, R. Siddalingappa, and A. Mane, “Gender Classification Based on Online Signature Features using Machine Learning Techniques”, Int J Intell Syst Appl Eng, vol. 10, no. 2, pp. 260–268, May 2022.

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Research Article