Title
Ethnicity classification based on gait using multi-view fusion
Abstract
The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy Image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs thebest. © 2010 IEEE.
Year
DOI
Venue
2010
10.1109/CVPRW.2010.5544614
CVPR Workshops
Field
DocType
Volume
Computer vision,Multilinear principal component analysis,Soft biometrics,Pattern recognition,Gait,Computer science,Feature extraction,Gait analysis,Artificial intelligence,Gait (human),Majority rule,Principal component analysis
Conference
null
Issue
ISSN
ISBN
null
null
978-1-4244-7029-7
Citations 
PageRank 
References 
8
0.45
18
Authors
3
Name
Order
Citations
PageRank
De Zhang1152.92
Yunhong Wang23816278.50
Bir Bhanu33356380.19