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General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
October 2007 (vol. 29 no. 10)
pp. 1700-1715
ASCII Text
x 
 
Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank, "General Tensor Discriminant Analysis and Gabor Features for Gait Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1700-1715, October, 2007.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2007.1096,
author = {Dacheng Tao and Xuelong Li and Xindong Wu and Stephen J. Maybank},
title = {General Tensor Discriminant Analysis and Gabor Features for Gait Recognition},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {29},
number = {10},
issn = {0162-8828},
year = {2007},
pages = {1700-1715},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.1096},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IS - 10
SN - 0162-8828
SP1700
EP1715
EPD - 1700-1715
A1 - Dacheng Tao,
A1 - Xuelong Li,
A1 - Xindong Wu,
A1 - Stephen J. Maybank,
PY - 2007
KW - Gabor Gait
KW - General Tensor Discriminant Analysis
KW - Human Gait Recognition
KW - Linear Discriminant Analysis
KW - Tensor Rank
KW - Visual Surveillance
VL - 29
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not.We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine state of the art classification methods in gait recognition.

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Index Terms:
Gabor Gait, General Tensor Discriminant Analysis, Human Gait Recognition, Linear Discriminant Analysis, Tensor Rank, Visual Surveillance
Citation:
Dacheng Tao, Xuelong Li, Xindong Wu, Stephen J. Maybank, "General Tensor Discriminant Analysis and Gabor Features for Gait Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1700-1715, Oct. 2007, doi:10.1109/TPAMI.2007.1096
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