Gait analysis for human walking paths and identities recognition

Authors:
Meng-Fen Ho;Ke-Zen Chen;Chung-Lin Huang
Affiliations:
Institute of Electrical Engineering, National Tsing Hua University, HsinChu and Dept. of Electronic Engineering, Hsiuping Institute of Technology,Taichung, Taiwan, R.O.C.;Institute of Electrical Engineering, National Tsing Hua University, HsinChu, Taiwan, R.O.C.;Institute of Electrical Engineering, National Tsing Hua University, HsinChu and Dept. of Informatics, Fo-Guang university, I-Lan, Taiwan, ROC
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 5
Cited 2

Individual Recognition Using Gait Energy Image

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Recognizing Humans Based on Gait Moment Image

SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Gait analysis for human identification through manifold learning and HMM

Pattern Recognition
Gait Recognition Using Radon Transform and Linear Discriminant Analysis

IEEE Transactions on Image Processing

A survey of advances in biometric gait recognition

CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
MMU GASPFA: A COTS multimodal biometric database

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a gait analysis method which extracts the dynamic and static information from human walking for walking path and identity recognition. First, we utilize the periodicity of swing distances to estimate the gait period for each gait sequence. For each gait cycle, we extract the dynamic information by analyzing the statistic histogram of motion vectors and static information using Fourier descriptors. The extracted information is transformed into lower dimensional embedding space to represent the subject. Given a test feature vector, the nearest neighbor classifier is applied to compare with the feature vectors in the gait database for human object identification. The proposed algorithm is evaluated on the CASIA gait database, and the experimental results demonstrate this new system achieves a high recognition rate.