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Proceedings of 2009 International Symposium on Computer Science and Computational Technology (ISCSCT 2009)

Huangshan, China, December 26-28, 2009

Editors: Fei Yu, Guangxue Yue, Jian Shu, Yun Liu

AP Catalog Number: AP-PROC-CS-09CN005

ISBN: 978-952-5726-07-7 (Print), 978-952-5726-08-4 (CD-ROM)

Page(s): 124-127

Gait Recognition Based on PCA and LDA

††††††† Qiong Cheng, Bo Fu, and Hui Chen

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This paper proposes a new gait recognition method using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is first applied to 1D time-varying distance signals derived from a sequence of silhouette images to reduce itís dimensionality. Then, LDA is performed to optimize the pattern class ificovtion. And, Spatiotemporal Correlation (STC) and Normalized Euclidean Distance (NED) are respectively used to measur the two different sequences and K nearest neighbor classification (KNN) are finally performed for recognition. The experimental results show the PCA and LDA based gait recognition algorithm is better than that based on PCA.

Index Terms

gait recognition; PCA; LDA; k-Nearest Neighbor method

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