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Gender Identification from Frontal Facial Images Using Multiresolution Statistical Descriptors

  • Prabha
  • Jitendra Sheetlani
  • Chitra Dhawale
  • Rajmohan Pardeshi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Gender identification is a significant task which is very useful in many computer applications like human–computer interaction, surveillance, demographic studies, and forensic studies. Being one of the most popular soft biometrics, gender information plays a vital role in improvement of the accuracy of biometric systems. In this paper, we have presented an approach based on multiresolution statistical descriptors derived from histogram of Discrete Wavelet Transform. First, the input facial image was enhanced by applying contrast limited adaptive histogram equalization. During feature extraction, multiresolution statistical descriptors were computed and fed into the Nearest Neighbor, Support Vector Machine, and Linear Discriminant Analysis classifiers respectively. We have achieved encouraging accuracy for gender identification on complex dataset of frontal facial images.

Keywords

Discrete wavelet transform Statistical features Gender identification Face image Support vector machine 

Notes

Acknowledgements

We are thankful to Smt. Savitri A. Nawade for participation in the creation of database for the experimentation work stated in this paper.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Prabha
    • 1
  • Jitendra Sheetlani
    • 1
  • Chitra Dhawale
    • 2
  • Rajmohan Pardeshi
    • 3
  1. 1.Department of Computer ScienceSSSUTMSSehore, BhopalIndia
  2. 2.P. R. Pote College of Engineering and ManagementAmravatiIndia
  3. 3.Department of Computer ScienceKarnatak CollegeBidarIndia

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