Analysis of Fingerprint Features: Ridge Information, Minutia Information and DWT Features for the Design of Gender Classifier Clusters

Authors

  • Chandrakant P. Divate Research Scholar Department of CSE, SECAB IET, Vijayapur, Visvesvaraya Technological University, Belgaum, Karnataka, India., Faculty of ATS Sanjay Bhokare Group Of Institutes Miraj.
  • Syed Abulhasan Quadri Professor, Department of CSE, SECAB IET, Vijayapur, Visvesvaraya Technological University, Belgaum, Karnataka, India.
  • A. Deepak Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu
  • Neeraj Varshney Department of Computer Engineering and Applications, GLA University, Mathura
  • Manish Sharma Associate Professor, Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand
  • Anurag Shrivastava Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamilnadu

Keywords:

Analysis, Fingerprint, Features, Ridge, Minutia, Discrete wavelet transform, DWT, Gender, Classification, Clusters

Abstract

Fingerprints offer a unique and incommutably into an existent's identity, including their gender. This paper introduces a method for gender identification using fingerprint features, including ridge information, minutiae information, and six-level discrete wavelet transform (DWT). The method was evaluated on a dataset of 100 individuals, with 50 male and 50 female samples. The proposed method first extracts the three features from the fingerprint images. Ridge information includes the minimum, maximum, and average ridge length. Minutiae information includes the ridge end count, ridge bifurcation count, and total ridge count. Six-level DWT is used to extract frequency features from the fingerprint images. Next, the features are clustered finger-wise into minimum, maximum, and average values for the male and female classes. These finger-wise clusters are then used to design a classifier for male and female. The proposed method achieved an accuracy of 88.28% for gender identification on the database of 100 individuals. The right ring finger was the most accurate finger for gender identification, with an accuracy of 95.46%. This simple and effective method for fingerprint-based gender identification achieved an accuracy of 88.28% on the database of 100 individuals. The method can be further improved by using a larger database and by extracting more features from the fingerprint images.

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References

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Published

24.03.2024

How to Cite

Divate, C. P. ., Quadri, S. A. ., Deepak, A. ., Varshney, N. ., Sharma, M. ., & Shrivastava, A. . (2024). Analysis of Fingerprint Features: Ridge Information, Minutia Information and DWT Features for the Design of Gender Classifier Clusters. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 138–151. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4959

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

License

Copyright (c) 2024 Chandrakant P. Divate, Syed Abulhasan Quadri, A. Deepak, Neeraj Varshney, Manish Sharma, Anurag Shrivastava

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