Gender Recognition Studying by Gait Energy Image Classification

Author

Juang, Li-Hong ; Lin, Shin-An ; Wu, Ming-Ni

Author_Institution

Dept. of Aerosp. & Syst. Eng., Feng Chia Univ., FCU, Taichung, Taiwan

fYear

2012

fDate

4-6 June 2012

Firstpage

837

Lastpage

840

Abstract

To detect human sex from complex background, illumination variations and objects by machine is very difficult but important for adaptive information service. In this research, we present a preliminary design and experimental results of gender recognition from walking movements that utilizes gait energy image(GEI) with denoised energy image(DEI) pre-processing as support vector machine(SVM) classifier to training and extract the characteristics. The result shows that the proposed method would adopt the few characteristic value but the accuracy can reach to 100%.

Keywords

feature extraction; image classification; image denoising; image motion analysis; object detection; support vector machines; DEI preprocessing; GEI; SVM classifier; adaptive information service; complex background; denoised energy image; gait energy image classification; gender recognition; human sex detection; illumination variation; support vector machine; walking movement; Accuracy; Feature extraction; Humans; Image recognition; Legged locomotion; Support vector machines; Training; Denoised energy image; Gait energy image; Support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer, Consumer and Control (IS3C), 2012 International Symposium on

Conference_Location

Taichung

Print_ISBN

978-1-4673-0767-3

Type

conf

DOI

10.1109/IS3C.2012.215

Filename

6228438