TY  - EJOU
AU  - Ling, Hefei  
AU  - Wu, Jia  
AU  - Li, Ping  
AU  - Shen, Jialie  

TI  - Attention-Aware Network with Latent Semantic Analysis for Clothing Invariant Gait Recognition
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 60
IS  - 3
SN  - 1546-2226

AB  - Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from the raw input. Thus, we propose a new CNN-based method which leverages advantage of the latent semantic analysis and attention mechanism. Based on discriminative features extracted using attention and the latent semantic analysis module respectively, multi-modal fusion method is proposed to fuse those features for its high fault tolerance in the decision level. Experiments on the most challenging clothing variation dataset: OU-ISIR TEADMILL dataset B show that our method outperforms other state-of-art gait approaches.
KW  - Gait recognition
KW  -  latent semantic analysis
KW  -  attention mechanism
KW  -  attention-aware neural network
KW  -  clothing-invariant
KW  -  feature fusion

DO  - 10.32604/cmc.2019.05605