Gender Classification Based on Online Signature Features using Machine Learning Techniques

Gornale, SS and Kumar, S and Siddalingappa, R and Mane, A (2022) Gender Classification Based on Online Signature Features using Machine Learning Techniques. In: International Journal of Intelligent Systems and Applications in Engineering, 10 (2). pp. 260-268.

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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.

Item Type:	Journal Article
Publication:	International Journal of Intelligent Systems and Applications in Engineering
Publisher:	Ismail Saritas
Additional Information:	The copyright for this article belongs to the Ismail Saritas.
Keywords:	Biometric Data Analysis; Feed Forward Deep Neural Network; Gender Classification; Online Handwritten Signature
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	27 Jun 2022 07:21
Last Modified:	27 Jun 2022 07:21
URI:	https://eprints.iisc.ac.in/id/eprint/73991

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