Real-time Gender Classification from Human Gait for Arbitrary View Angles

Author

Chang, Ping-Chieh ; Tien, Ming-Chun ; WU, JA-LING ; Hu, Chuan-Shen

Author_Institution

Grad. Inst. of Networking & Multimedia, Nat. Taiwan Univ., Taipei, Taiwan

fYear

2009

fDate

14-16 Dec. 2009

Firstpage

88

Lastpage

95

Abstract

In this paper, we investigate an important but understudied problem, gender classification from human gaits. And we have proved the ability of using GEI (Gait Energy Image) as a representation of human gait for arbitrary view angles. Using GEI as a discriminative feature, we construct angle classifiers and gender classifiers from different approaches. Experiments show that our system achieved a good performance in real-time and is able to be applied to real-world application.

Keywords

computer vision; gait analysis; image classification; image motion analysis; image representation; angle classifiers; gait energy image; gender classification; human gait representation; Application software; Computer vision; Data mining; Face detection; Humans; Legged locomotion; Principal component analysis; Real time systems; Support vector machine classification; Support vector machines; Fisher-Boosting; GEI (Gait Energy Image); Gender classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Multimedia, 2009. ISM '09. 11th IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

978-1-4244-5231-6

Electronic_ISBN

978-0-7695-3890-7

Type

conf

DOI

10.1109/ISM.2009.81

Filename

5364846