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

Sneha Choudhary; Chandra Prakash; Rajesh Kumar

doi:10.1109/ICACCI.2017.8126100

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 proceeding › Conference contribution

7 Citations (Scopus)

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 language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1767-1771
Number of pages	5
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8126100
Publication status	Published - 30-11-2017
Externally published	Yes
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country/Territory	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

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10.1109/ICACCI.2017.8126100

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

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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.",

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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 proceeding › Conference contribution

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N2 - 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.

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

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