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Gender Classification from Face Images Based on Gradient Directional Pattern (GDP)

  • Faisal AhmedEmail author
  • Padma Polash Paul
  • Patrick Wang
  • Marina Gavrilova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9156)

Abstract

This paper presents an appearance-based facial feature descriptor based on the gradient directional pattern (GDP) for gender classification from face images. The GDP operator encodes the texture information of a local neighborhood by quantizing the gradient directions of the neighbors with respect to the center. The facial feature descriptor is computed by first dividing the face image into a number of sub-regions and then concatenating the individual GDP histograms computed from the corresponding sub-regions. Then, principal component analysis (PCA) is applied on the obtained face descriptor in order to reduce the feature dimensionality. We use a support vector machine (SVM) for the classification task. Experimental analysis on a large database comprising 1800 facial images shows promising results for the proposed method, as compared to some well-known appearance-based face descriptors.

Keywords

Gender classification Gradient directional pattern (GDP) Principal component analysis (PCA) Support vector machine (SVM) 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Faisal Ahmed
    • 1
    Email author
  • Padma Polash Paul
    • 1
  • Patrick Wang
    • 2
  • Marina Gavrilova
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.College of Computer and Information ScienceNortheastern UniversityBostonUSA

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