Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/61569
Title: Gender classification on 2D human skeleton
Authors: Barra, Paola
Bisogni, Carmen
Nappi, Michele
Freire-Obregon, David 
Castrillon-Santana, Modesto 
NaitAli, A
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Gender classification
Gait analysis
Supervised learning
Issue Date: 2019
Conference: 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART) 
3rd International Conference on Bio-Engineering for Smart Technologies, BioSMART 2019 
Abstract: Soft bimetrics has become a trending research topic over the past decade. In last years, the increase of new technologies such as the wearable camera devices has introduced a new challenge into the gender classification problem. In this sense, the ability to classify the gender not by an image but by the 2D estimated skeleton points is considered in this paper. Our experiments show that the human gender can be classified just considering the pose information provided by the body pose information. The proposed method have shown a remarkable performance on a dataset where subjects and camera are in movement.
URI: http://hdl.handle.net/10553/61569
DOI: 10.1109/BIOSMART.2019.8734198
Source: 2019 3rd International Conference On Bio-Engineering For Smart Technologies (Biosmart)
Appears in Collections:Actas de congresos
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