AI Intelligence-based Gender Classification using Biometric- Digital Signature Feature Extraction Methods

Authors

  • S. Siraj M.Tech Student, Department of Computer Science and Engineering, G. Pullaiah College of Engineering and Technology, (Autonomous), Kurnool
  • Prem Kumar Singuluri Sr. Professor and Dean Innovations, Department of Computer Science and Engineering, G. Pullaiah College of Engineering and Technology, Kurnool
  • M. Rudra Kumar Professor, Department of CSE, GPCET, Kurnool

Keywords:

Machine Learning Algorithms, Training, Testing, Biometrics, Modeling

Abstract

A person's signature can reveal much about their health, career path, and current state of mind. From a biometrics point of view, a person's gender is a demographic category like a soft trait, while a Handwritten Signature is a behavioural trait. Numerous fields, including forensics and psychology, have alluded to the possibility of gender classification based on handwritten signatures. Feminist aesthetics can be found in works written by men with a high degree of intraclass variation and vice versa. This provides evidence for using a signature to determine a person's gender. Extraction of numerical features from male and female dynamic trademark samples forms the basis of the proposed method. Five hundred thirty-five people of varying ages were surveyed. These signature examples were then transformed into numerical attributes, yielding more than 60 signature features for each dataset. Six distinct Machine Learning approaches were used in the experiments; Overall, these techniques achieved an accuracy of 78% (KNN), 83% (LR), 73% (Poly kernel- SVM), and 51% (RBF kernel in SVM). In contrast, a Poly kernel trained with cross-validation achieved 85% (SVM), 91% (DT), 97% (RF) and 98% with Deep Neural nets. In summary, deep neural networks performed best, followed closely by RF.

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Published

15.10.2022

How to Cite

[1]
S. . Siraj, P. K. . Singuluri, and M. R. . Kumar, “AI Intelligence-based Gender Classification using Biometric- Digital Signature Feature Extraction Methods”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 262 –, Oct. 2022.