关键词: 步态识别, 平均步态能量图, 核主成分分析, 特征提取

Abstract: Concerning the issue of extracting features more efficiently from a sequence of gait frames and real-time recognition, an effective human recognition method based on Mean Gait Energy Image (MGEI) was described, which utilized Kernel Principal Component Analysis (KPCA). A pre-processing technique was used to segment the moving silhouette from the walking figure. The algorithm obtained the gait quasi-periodicity through analyzing the width information of the lower limbs' gait contour edge, and the MGEI was calculated from gait period. KPCA extracted principal component with nonlinear method and described the relationship among three or more pixels of the identified images. In this paper, KPCA could make use of the high correlation between different MGEIs for feature extraction by selecting the proper kernel function, and Euclidean distance weighted by variance reciprocal was designed as the classifier. The experimental results show that the proposed approach has better recognition performance and the computation time is greatly reduced.

Key words: gait recognition, Mean Gait Energy Image (MGEI), Kernel Principal Component Analysis (KPCA), feature extraction