Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

Tee Connie; Michael Kah Ong Goh; Andrew Beng Jin Teoh

doi:10.1109/ICASSP.2013.6638206

Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

Tee Connie, Michael Kah Ong Goh, Andrew Beng Jin Teoh

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.

Original language	English
Title of host publication	2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages	2989-2993
Number of pages	5
DOIs	https://doi.org/10.1109/ICASSP.2013.6638206
Publication status	Published - 2013 Oct 18
Event	2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada Duration: 2013 May 26 → 2013 May 31

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Other

Other	2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/Territory	Canada
City	Vancouver, BC
Period	13/5/26 → 13/5/31

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2013.6638206

Cite this

Connie, T., Goh, M. K. O., & Teoh, A. B. J. (2013). Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 2989-2993). [6638206] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638206

Connie, Tee ; Goh, Michael Kah Ong ; Teoh, Andrew Beng Jin. / Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings. 2013. pp. 2989-2993 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis",

abstract = "One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.",

author = "Tee Connie and Goh, {Michael Kah Ong} and Teoh, {Andrew Beng Jin}",

year = "2013",

month = oct,

day = "18",

doi = "10.1109/ICASSP.2013.6638206",

language = "English",

isbn = "9781479903566",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "2989--2993",

booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",

note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",

}

Connie, T, Goh, MKO & Teoh, ABJ 2013, Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. in 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings., 6638206, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 2989-2993, 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, 13/5/26. https://doi.org/10.1109/ICASSP.2013.6638206

Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. / Connie, Tee; Goh, Michael Kah Ong; Teoh, Andrew Beng Jin.

2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings. 2013. p. 2989-2993 6638206 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

AU - Connie, Tee

AU - Goh, Michael Kah Ong

AU - Teoh, Andrew Beng Jin

PY - 2013/10/18

Y1 - 2013/10/18

N2 - One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.

AB - One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.

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DO - 10.1109/ICASSP.2013.6638206

M3 - Conference contribution

AN - SCOPUS:84890519018

SN - 9781479903566

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 2989

EP - 2993

BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings

T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013

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Connie T, Goh MKO, Teoh ABJ. Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings. 2013. p. 2989-2993. 6638206. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638206

Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

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