Gait identification using a novel gait representation: Radon transform of mean gait energy image

Authors:
Farhad Bagher Oskuie;Karim Faez;Ali Cheraghian;Hamidreza Dastmalchi
Affiliations:
Dept. Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;Dept. Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;Dept. Electrical Engineering, Amirkabir University of Technology, Tehran, Iran;Dept. Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Venue:
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Year:
2011

Citing 6
Cited 0

A Multi-view Method for Gait Recognition Using Static Body Parameters

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Gait Analysis for Recognition and Classification

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image

IEEE Transactions on Pattern Analysis and Machine Intelligence
Human gait recognition by the fusion of motion and static spatio-temporal templates

Pattern Recognition
Gait Recognition Using Radon Transform and Linear Discriminant Analysis

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gait is one of the most practical biometric techniques which present the capability to recognize individuals from distance. In this study, we propose a novel gait template based on Radon Transform of Mean Gait Energy Image, as RTMGEI. Robustness against image noises and reducing data dimensionality can be achieved by using Radon Transform, as well as capturing variations of Mean Gait Energy Images (MGEIs) over their centers. Feature extraction is done by applying the Zernike moments to RTMGEIs. Orthogonal property of Zernike moment basis functions guarantees the statistically independence of coefficients in extracted feature vectors. The Euclidean minimum distance is used as the classifier. The our proposed method is evaluated on the CASIA database. Results show that our method outperforms recently presented works due to its high performance.