A hybrid approach for gait based gender classification using GEI and spatio temporal parameters

Sneha Choudhary, Chandra Prakash, Rajesh Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Gender classification play a significant role in recognition performance. For the purpose of visual surveillance, gender is considered as an important factor. In this paper a hybrid approach is proposed by fusing Gait Energy Image (GEI) with spatio temporal parameters for the gender classification. The dataset used is CASIA B which comprises of 118 subjects (89 males and 29 females). The proposed method consists of four steps. Gait Energy Image (GEI) is obtained by normalizing and averaging all the silhouette images in one gait cycle for all the subjects. The dimensions of GEI image is reduced by using principal component analysis. 5 spatio temporal parameters namely cadence, speed, height, stride length, stance period are calculated and concatenated with the reduced GEI Image. The reduced feature vector set is trained and tested using support vector machine and artificial neural network. Maximum accuracy achieved is 98.16% which shows the highly competitive results compared to previous methods.

Original languageEnglish
Title of host publication2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1767-1771
Number of pages5
Volume2017-January
ISBN (Electronic)9781509063673
DOIs
Publication statusPublished - 30-11-2017
Externally publishedYes
Event2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India
Duration: 13-09-201716-09-2017

Conference

Conference2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
CountryIndia
CityManipal, Mangalore
Period13-09-1716-09-17

Fingerprint

Principal component analysis
Support vector machines
Neural networks

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Choudhary, S., Prakash, C., & Kumar, R. (2017). A hybrid approach for gait based gender classification using GEI and spatio temporal parameters. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 1767-1771). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126100
Choudhary, Sneha ; Prakash, Chandra ; Kumar, Rajesh. / A hybrid approach for gait based gender classification using GEI and spatio temporal parameters. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1767-1771
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Choudhary, S, Prakash, C & Kumar, R 2017, A hybrid approach for gait based gender classification using GEI and spatio temporal parameters. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1767-1771, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8126100

A hybrid approach for gait based gender classification using GEI and spatio temporal parameters. / Choudhary, Sneha; Prakash, Chandra; Kumar, Rajesh.

2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1767-1771.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Choudhary S, Prakash C, Kumar R. A hybrid approach for gait based gender classification using GEI and spatio temporal parameters. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1767-1771 https://doi.org/10.1109/ICACCI.2017.8126100