Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/59992
Title: Gait analysis for gender classification in forensics
Authors: Barra, Paola
Bisogni, Carmen
Nappi, Michele
Freire-Obregón, David 
Castrillón-Santana, Modesto 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Gender Classification
Gait analysis
Supervised learning
SVC
Random forest, et al
Issue Date: 2019
Journal: Communications in Computer and Information Science 
Conference: 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications, DependSys 2019 
Abstract: Gender Classification (GC) is a natural ability that belongs to the human beings. Recent improvements in computer vision provide the possibility to extract information for different classification/recognition purposes. Gender is a soft biometrics useful in video surveillance, especially in uncontrolled contexts such as low-light environments, with arbitrary poses, facial expressions, occlusions and motion blur. In this work we present a methodology for the construction of a gait analyzer. The methodology is divided into three major steps: (1) data extraction, where body keypoints are extracted from video sequences; (2) feature creation, where body features are constructed using body keypoints; and (3) classifier selection when such data are used to train four different classifiers in order to determine the one that best performs. The results are analyzed on the dataset Gotcha, characterized by user and camera either in motion.
URI: http://hdl.handle.net/10553/59992
ISBN: 978-981-15-1303-9
ISSN: 1865-0929
DOI: 10.1007/978-981-15-1304-6_15
Source: Communications in Computer and Information Science [ISSN 1865-0929], v. 1123 CCIS, p. 180-190
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