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.
This work has partially been supported by projects CSD2007-00018 and CICYT TIN2009-14205-C04-04 from the Spanish Ministry of Innovation and Science, P1-1B2009-04 from Fundació Caixa Castelló-Bancaixa and PREDOC/2008/04 grant from Universitat Jaume I. Portions of this research use the CASIA Gait Database collected by Institute of Automation, Chinese Academy of Sciences.
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Martín-Félez, R., Mollineda, R.A., Sánchez, J.S. (2012). Gender Classification from Pose-Based GEIs. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_60
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