Title
Gait recognition using a view transformation model in the frequency domain
Abstract
Gait analyses have recently gained attention as methods of identification of individuals at a distance from a camera. However, appearance changes due to view direction changes cause difficulties for gait recognition systems. Here, we propose a method of gait recognition from various view directions using frequency-domain features and a view transformation model. We first construct a spatio-temporal silhouette volume of a walking person and then extract frequency-domain features of the volume by Fourier analysis based on gait periodicity. Next, our view transformation model is obtained with a training set of multiple persons from multiple view directions. In a recognition phase, the model transforms gallery features into the same view direction as that of an input feature, and so the features match each other. Experiments involving gait recognition from 24 view directions demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2006
10.1007/11744078_12
ECCV (3)
Keywords
Field
DocType
frequency-domain feature,various view direction,frequency domain,gait analysis,gait recognition system,view transformation model,view direction,gait periodicity,multiple view direction,recognition phase,gait recognition,fourier analysis
Frequency domain,Computer vision,Fourier analysis,Gait,Computer science,Silhouette,Gesture recognition,Image processing,Perspective (graphical),Gait analysis,Artificial intelligence
Conference
Volume
ISSN
ISBN
3953
0302-9743
3-540-33836-5
Citations 
PageRank 
References 
160
5.14
20
Authors
5
Search Limit
100160
Name
Order
Citations
PageRank
Yasushi Makihara1101270.67
Ryusuke Sagawa263152.61
Yasuhiro Mukaigawa347853.31
Tomio Echigo434825.41
Yasushi Yagi51752186.22