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http://hdl.handle.net/10609/92454
Title: Robust gait-based gender classification using depth cameras
Author: Igual, Laura
Lapedriza Garcia, Àgata  
Borràs, Ricard
Others: Universitat de Barcelona
Universitat Autònoma de Barcelona
Universitat Oberta de Catalunya (UOC)
Keywords: linear discriminant analysis
gait feature
gait recognition
depth camera
Issue Date: 2-Jan-2013
Publisher: EURASIP Journal on Image and Video Processing
Citation: Igual, L., Lapedriza, A. & Borràs, R. (2013). Robust gait-based gender classification using depth cameras. EURASIP Journal on Image and Video Processing, 2013(1). doi: 10.1186/1687-5281-2013-1
Also see: https://jivp-eurasipjournals.springeropen.com/articles/10.1186/1687-5281-2013-1
Abstract: This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section.
Language: English
URI: http://hdl.handle.net/10609/92454
ISSN: 1687-5176MIAR
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