Paper Info
Title
Robust Clothing-Invariant Gait Recognition
Abstract
Robust gait recognition is a challenging problem, due to the large intra-subject variations and small inter-subject variations. Out of the covariate factors like shoe type, carrying condition, elapsed time, it has been demonstrated that clothing is the most challenging covariate factor for appearance-based gait recognition. For example, long coat may cover a significant amount of gait features and make it difficult for individual recognition. In this paper, we proposed a random subspace method (RSM) framework for clothing-invariant gait recognition by combining multiple inductive biases for classification. Even for small size training set, this method can achieve promising performance. Experiments are conducted on the OU-ISIR Treadmill dataset B which includes 32 combinations of clothing types, and the average recognition accuracy is more than 80%, which indicates the effectiveness of our proposed method.
Year
DOI
Venue
2012
10.1109/IIH-MSP.2012.84
IIH-MSP
Keywords
Field
DocType
challenging covariate factor,robust gait recognition,random subspace method,average recognition accuracy,individual recognition,clothing-invariant gait recognition,challenging problem,gait feature,robust clothing-invariant gait recognition,appearance-based gait recognition,gait analysis,databases,principal component analysis,biometrics,covariance matrix,clothing,learning artificial intelligence,overfitting
Computer vision,Covariate,Gait,Pattern recognition,Random subspace method,Computer science,Gait analysis,Artificial intelligence,Biometrics,Overfitting,Covariance matrix,Principal component analysis
Conference
Citations 
PageRank 
References 
20
0.77
6
Authors
3
Name
Order
Citations
PageRank
Yu Guan1275.06
Chang-Tsun Li293772.14
Yongjian Hu350230.74