Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis and its application to gait recognition

In this paper, a novel algorithm for feature extraction —Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA) is proposed. The improved SpC2DLPPCA algorithm over C2DLPPCA and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefits greatly to three points: (1) SpC2DLPPCA can overcome a failing that larger dimension matrix may bring about more consuming time on computing its eigenvalues and eigenvectors. (2) SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact the expression of features. Finally, experiments on the CASIA(B) gait database show that SpC2DLPPCA has higher recognition accuracies than C2DLPPCA and SpC2DPCA.

[1]  Jian Yang,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Dewen Hu,et al.  Comment on: "Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition" , 2008, Pattern Recognit..

[3]  Wang Ke.  Subpattern-Based Complete Two Dimensional Principal Component Analysis for Gait Recognition , 2009 .

[4]  Bin Luo,et al.  2D-LPP: A two-dimensional extension of locality preserving projections , 2007, Neurocomputing.

[5]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

[6]  Xin Jin,et al.  Complete Two-Dimensional PCA for Face Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[7]  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).

[8]  Songcan Chen,et al.  Adaptively weighted sub-pattern PCA for face recognition , 2005, Neurocomputing.

[9]  Daoqiang Zhang,et al.  (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition , 2005, Neurocomputing.

[10]  Zilan Hu,et al.  Comment on: "Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition" , 2008, Pattern Recognit..

[11]  Jieping Ye,et al.  Generalized Low Rank Approximations of Matrices , 2004, Machine Learning.

[12]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Yulian Zhu,et al.  Subpattern-based principle component analysis , 2004, Pattern Recognit..

[14]  P. Nagabhushan,et al.  (2D)2 FLD: An efficient approach for appearance based object recognition , 2006, Neurocomputing.