DocumentCode
2151277
Title
Gait recognition using sparse representation
Author
Ma, Yan
Author_Institution
Dept. of Inf. Eng., Lanzhou Univ. of Finance & Econ., Lanzhou, China
fYear
2010
fDate
11-14 July 2010
Firstpage
136
Lastpage
139
Abstract
A new approach of gait recognition based on sparse representation is proposed. Static and motion information are fused using the averaged boundary which is extracted by canny operator. The training data and the testing data belong to one object when there is a sparse representation in training data for the testing data. The algorithm is implemented on USF gait database. Experimental results prove the higher performance of the method on the gait datasets which are captured on different time.
Keywords
gait analysis; object recognition; visual databases; USF gait database; averaged boundary; canny operator; gait recognition; motion information; sparse representation; static information; testing data; training data; Dictionaries; Hidden Markov models; Matching pursuit algorithms; Pattern recognition; Testing; Training data; Wavelet analysis; Averaged boundary; Gait recognition; Sparse representation; Static and motion;
fLanguage
English
Publisher
ieee
Conference_Titel
Wavelet Analysis and Pattern Recognition (ICWAPR), 2010 International Conference on
Conference_Location
Qingdao
Print_ISBN
978-1-4244-6530-9
Type
conf
DOI
10.1109/ICWAPR.2010.5576306
Filename
5576306
Link To Document