Robust view transformation model for gait recognition

Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method...

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Bibliographic Details
Main Authors: Shuai Zheng, Junge Zhang, Kaiqi Huang, Ran He, Tieniu Tan
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Feature extraction
Gait Recognition
Image recognition
Legged locomotion
Low-rank
Probes
Robustness
Vectors
View Transformation Model
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Summary:Recent gait recognition systems often suffer from the challenges including viewing angle variation and large intra-class variations. In order to address these challenges, this paper presents a robust View Transformation Model for gait recognition. Based on the gait energy image, the proposed method establishes a robust view transformation model via robust principal component analysis. Partial least square is used as feature selection method. Compared with the existing methods, the proposed method finds out a shared linear correlated low rank subspace, which brings the advantages that the view transformation model is robust to viewing angle variation, clothing and carrying condition changes. Conducted on the CASIA gait dataset, experimental results show that the proposed method outperforms the other existing methods.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2011.6115889