Human Gait Recognition Based on Kernel PCA Using Projections

作     者：Murat Ekinci Murat Aykut 

作者机构：Computer Vision LabDepartment of Computer EngineeringKaradeniz Technical University61080 TrabzonTurkey 

基　　金：This work was supported by Karadeniz Technical University tinder Grant No.KTU-2004.112.009.001 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2007年第22卷第6期

页      码：867-876页

摘      要：This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis （PCA）, to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles.

主 题 词：biometrics gait recognition gait representation kernel PCA pattern recognition 

学科分类：08[工学] 080203[080203] 0802[工学-机械学] 

核心收录：

D　O　I：10.1007/s11390-007-9101-z

馆 藏 号：203317103...