Gender Classification Using Faces and Gaits

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 240)

Abstract

Gender classification is one of the challenging problems in computer vision. Many interactive applications need to exactly recognize human genders. In this paper, we are carrying out some experiments to classify the human gender in conditions of low captured video resolution. We use Local Binary Pattern, Gray Level Co-occurrence Matrix to extract the features from faces and Gait Energy Motion, Gait Energy Image for gaits. We propose to combine face and gait features with the combination classifier to enhance gender classification performance.

Keywords

Gender Classification Faces Gaits Local binary pattern Gait Energy Motion Gait Energy Image 

Notes

Acknowledgments

This work (Grants No. C0005448) was supported by Business for Cooperative R&D between industry, Academy, and Research Institute funded by Korea Small and Medium Business Administration in 2012.

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Copyright information

© Springer Science+Business Media Dordrecht(Outside the USA) 2013

Authors and Affiliations

  1. 1.Department of Information and Communication EngineeringMyongji UniversityYonginSouth Korea

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