Imperial College London
Dr Panagiota (Tania) Stathaki

Dr Panagiota (Tania) Stathaki

Faculty of Engineering, Department of Electrical and Electronic Engineering

Reader in Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6229t.stathaki Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

812Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhao:2016:10.1016/j.neucom.2015.07.012,
author = {Zhao, X and Jiang, Y and Stathaki, T and Zhang, H},
doi = {10.1016/j.neucom.2015.07.012},
journal = {Neurocomputing},
pages = {530--540},
title = {Gait recognition method for arbitrary straight walking paths using appearance conversion machine},
url = {http://dx.doi.org/10.1016/j.neucom.2015.07.012},
volume = {173},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We investigate the problem of multi-view human gait recognition along any straight walking paths. It is observed that the gait appearance changes as the view changes while certain amount of correlated information exists among different views. Taking advantage of that type of correlation, a multi-view gait recognition method is proposed in this paper. First, we estimate the viewing angle of the monitor equipment in terms of the probe subject. To this end, our method considers this as a classification problem, where the classification signals are the viewing angles, and the classification features are the elements of the transformation matrix that is estimated by the Transformation Invariant Low-Rank Texture (TILT) algorithm. Then, the gallery gait appearances are converted to the view of the probe subject using the proposed Appearance Conversion Machine (ACM), where the gait features of the spatially neighbouring pixels of the gait feature are considered as the correlated information of the two views. In the end, a similarity measurement is applied on the converted gait appearance and the testing gait appearance. Experiments on the CASIA-B multi-view gait database show that the proposed gait recognition method outperforms the state-of-the-art under most views.
AU  - Zhao,X
AU  - Jiang,Y
AU  - Stathaki,T
AU  - Zhang,H
DO  - 10.1016/j.neucom.2015.07.012
EP  - 540
PY  - 2016///
SN  - 0925-2312
SP  - 530
TI  - Gait recognition method for arbitrary straight walking paths using appearance conversion machine
T2  - Neurocomputing
UR  - http://dx.doi.org/10.1016/j.neucom.2015.07.012
UR  - http://hdl.handle.net/10044/1/53235
VL  - 173
ER  -
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