Human gait based gender identification system using Hidden Markov Model and Support Vector Machines

Author

Das, Deepjoy ; Chakrabarty, Alok

Author_Institution

Dept. of Comput. Sci. & Eng., Nat. Inst. of Technol. Meghalaya, Shillong, India

fYear

2015

fDate

15-16 May 2015

Firstpage

268

Lastpage

272

Abstract

The paper presents an approach towards human gender recognition system. The Silhouettes from Center for Biometrics and Security Research (CASIA) gait database are segmented in order to identify major body points and to generate corresponding point-light display. The features such as two dimensional coordinates of major body points and joint angles are extracted from the point-light display. The features are classified using Hidden Markov Model (HMM) and Support Vector Machines (SVM). The study yields a recognition rate of 69.18% and 76.79% with 100 subject data using HMM and SVM respectively. There has been a significant improvement in recognition accuracy using joint angles as the features.

Keywords

feature extraction; gait analysis; hidden Markov models; image classification; image segmentation; support vector machines; visual databases; CASIA gait database; Center for Biometrics and Security Research; HMM; SVM; feature classification; feature extraction; hidden Markov model; human gait based gender identification system; human gender recognition system; joint angles; major body point identification; point-light display; recognition rate; silhouette segmentation; support vector machines; two-dimensional coordinates; Foot; Hidden Markov models; Joints; Kernel; Knee; Support vector machines; Gender Recognition/Identification; Hidden Markov Model (HMM); Human Gait; Point-light (PL) display; Support Vector Machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication & Automation (ICCCA), 2015 International Conference on

Conference_Location

Noida

Print_ISBN

978-1-4799-8889-1

Type

conf

DOI

10.1109/CCAA.2015.7148386

Filename

7148386