Gender Classification from Hand Shape

  • RUpali Vishwanath Gajbhiye University of Pune
  • A b Diggikar
Keywords: ZM, FD, GF, classification, fusion

Abstract

—Gender classification is important in identify the gender of a criminals and also minimize the list of suspects search from hand shape. It is also useful for of female security purpose. Using a small dataset of 50-hand shape images for 25 persons of different ages and genders is analyzed. By considering some geometry, region & boundary features it is possible to distinguish male and female. Features extraction can be done by using three methods Gabor filter, Zernike moment and Fourier descriptor. The features of each hand is represented by using higher order ZM (Zernike moment), FD (Fourier descriptor) and GF (gabbro filter). Finally, comparison is done by using feature level fusion techniques. The proposed system is implemented in MATLAB and has achieved accuracy of 91.99 % average.

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Published
2018-03-23
How to Cite
Gajbhiye, R., & Diggikar, A. (2018). Gender Classification from Hand Shape. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 1(1). Retrieved from http://asianssr.org/index.php/ajct/article/view/72
Section
Article

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