Gait Recognition Using WPD and (2D)²PCA

To improve the gait recognition rate,a gait recognition method based on wavelet packet decomposition(WPD) and two-directional two-dimensional principal component analysis((2D) ²PCA) was proposed.In the method,gait energy image(GEI) of the body silhouette was firstly adopted to solve the problem of huge gait data.And then,WPD and(2D) ²PCA were used to extract features to solve the problem existing in the gait recognition method based on wavelet transform at present: high frequency component loss or high dimension problem.The Experiment evaluation was conducted on NLPR gait database and compared with classical methods.Result shows that the proposed method has a higher recognition rate and is more robust to the change of view.