Gait recognition based on Procrustes mean shape and Fan-Beam transform

To address the limitations of gait recognition based on a single feature, complementary dynamic and static information is integrated for more accurate identification. Initially, Procrustes mean shape is utilized to extract static features of gait silhouette contours. Then, gait energy image (GEI) is computed, followed by Fan-Beam transformation of GEI. Twodimensional principal component analysis is employed for feature space dimensionality reduction to obtain frequency dynamic features of the moving target. Finally, the Euclidean distances of the two features are fused to achieve the ultimate recognition outcome. Experimental verification on Dataset B from the Chinese Academy of Sciences demonstrates the expected recognition performance.