This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Human Identification Using Temporal Information Preserving Gait Template
Nov. 2012 (vol. 34 no. 11)
pp. 2164-2176
Chen Wang, Shanghai Key Lab. of Intell. Inf. Process., Fudan Univ., Shanghai, China
Junping Zhang, Shanghai Key Lab. of Intell. Inf. Process., Fudan Univ., Shanghai, China
Liang Wang, Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China
Jian Pu, Shanghai Key Lab. of Intell. Inf. Process., Fudan Univ., Shanghai, China
Xiaoru Yuan, Key Lab. of Machine Perception, Peking Univ., Beijing, China
Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency.
Index Terms:
Hidden Markov models,Feature extraction,Legged locomotion,Humans,Image recognition,Data mining,Computational modeling,pattern recognition,Computer vision,gait recognition,biometric authentication
Citation:
Chen Wang, Junping Zhang, Liang Wang, Jian Pu, Xiaoru Yuan, "Human Identification Using Temporal Information Preserving Gait Template," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2164-2176, Nov. 2012, doi:10.1109/TPAMI.2011.260
Usage of this product signifies your acceptance of the Terms of Use.