Gait Optical Flow Image Decomposition for Human Recognition
As a behavioral biometric,gait recognition has gained an increased interest in recent years because it can operates without subject cooperation and from a distance.This paper presents a novel gait feature extracting approach based on gait optical flow image (GOFI) decomposition.The variation of algebraic sum of all vertical optical flow components is used to detect gait cycles.We calculate sums of horizontal and vertical optical flow components that greater than 0,respectively,for each row and column of GOFI to obtain four feature vectors of the subject.By exploiting principle component analysis (PCA) for the feature vectors to compute a PCA subspace that has the largest variance associated with them,then linear discriminant analysis (LDA) for the subspace to compute a LDA subspace that discriminates among the PCA subspace.Experiments implemented on the CASIA Database B and C demonstrate the approach achieves a 98% recognition rate under normal walking condition,while a promising performance under the influence of other covariate factors.
gait recognition optical flow PCA LDA feature fusion
Zhengping Luo Tianqi Yang Yanjun Liu
Department of Information Science and Technology Jinan University,Guangzhou,China
国际会议
重庆
英文
581-586
2016-03-20（万方平台首次上网日期，不代表论文的发表时间）