Robust Clothing-Invariant Gait Recognition

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.

[1]  M. P. Murray.  Gait as a total pattern of movement. , 1967, American journal of physical medicine.

[2]  Yasushi Makihara,et al.  Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control , 2010, Pattern Recognit..

[3]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Yonghuai Liu,et al.  Improving ICP with easy implementation for free-form surface matching , 2004, Pattern Recognit..

[5]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[6]  Murray Mp,et al.  Gait as a total pattern of movement. , 1967 .

[7]  Mark S. Nixon,et al.  The Effect of Time on Gait Recognition Performance , 2012, IEEE Transactions on Information Forensics and Security.

[8]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Mark S. Nixon,et al.  On a Large Sequence-Based Human Gait Database , 2004 .

[11]  Z. Liu,et al.  Simplest representation yet for gait recognition: averaged silhouette , 2004, ICPR 2004.

[12]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Tieniu Tan,et al.  A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).