Gender classification from pose-based GEIs

Authors:
Raúl Martín-Félez;Ramón A. Mollineda;J. Salvador Sánchez
Affiliations:
Institute of New Imaging Technologies (INIT), Universitat Jaume I., Castelló de la Plana, Spain;Institute of New Imaging Technologies (INIT), Universitat Jaume I., Castelló de la Plana, Spain;Institute of New Imaging Technologies (INIT), Universitat Jaume I., Castelló de la Plana, Spain
Venue:
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Year:
2012

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Abstract

This paper introduces a new approach for gait-based gender classification in which some key biomechanical poses of a gait pattern are represented by partial Gait Energy Images (GEIs). These pose-based GEIs can more accurately represent the shape of the body parts and some dynamic features with respect to the usually blurred depiction provided by a general GEI comprising all poses. Gait-based gender classification is based on the weighted decision fusion of the pose-based GEIs. Results of experiments on two large gait databases prove that this method performs significantly better than clasiffiers based on the original GEI.