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. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Martín-Félez, R., Mollineda, R. A., & Sánchez, J. S. (2012). Gender classification from pose-based GEIs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7594 LNCS, pp. 501–508). Springer Verlag. https://doi.org/10.1007/978-3-642-33564-8_60
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