On view-invariant gait recognition: A feature selection solution

Ning Jia; Victor Sanchez; Chang Tsun Li

doi:10.1049/iet-bmt.2017.0151

On view-invariant gait recognition: A feature selection solution

Ning Jia, Victor Sanchez, Chang Tsun Li

Research output: Book chapter/Published conference paper › Conference paper › peer-review

18 Citations (Scopus)

8 Downloads (Pure)

Abstract

The authors present an improved feature selection solution for the view-invariant gait recognition problem, based on their previously proposed method called view-invariant feature selector (ViFS), which automatically reconstruct an optimised gallery template from a set of multi-view gallery templates. They improved ViFS by introducing a constraint to make sure that the reconstructed features have the same scale as the original features, thus reducing the number of misclassifications caused by data misalignment. They evaluate the improved ViFS on the CASIA B and OU-ISIR large population datasets by performing a wide range of comparative studies in order to explore and confirm its effectiveness. Evaluation results indicate that the proposed framework is very effective for view-invariant gait recognition tasks.

Original language	English
Title of host publication	IET biometrics
Subtitle of host publication	Special issue: Selected papers from the international workshop on biometrics and forensics (IWBF2017)
Editors	Michael Fairhurst
Place of Publication	United Kingdom
Publisher	Institution of Engineering and Technology (IET)
Pages	287-295
Number of pages	9
Volume	7
Edition	4
DOIs	https://doi.org/10.1049/iet-bmt.2017.0151
Publication status	Published - 25 Jun 2018
Event	5th International Workshop on Biometrics and Forensics: IWBF 2017 - University of Warwick, Coventry, United Kingdom Duration: 04 Apr 2017 → 05 Apr 2017 https://warwick.ac.uk/fac/sci/dcs/people/chang-tsun_li/iwbf2017/ (Conference website)

Publication series

Name	IET Biometrics
Publisher	The Institution of Engineering and Technology
ISSN (Print)	2047-4938

Conference

Conference	5th International Workshop on Biometrics and Forensics
Country/Territory	United Kingdom
City	Coventry
Period	04/04/17 → 05/04/17
Other	IWBF is an international forum devoted specifically to facilitate synergies in research and development in the areas of multimedia forensics, forensicbiometrics and forensic science. IWBF provides the meeting place for those concerned with the usage of multimedia analysis in forensic applications and biometric recognition systems, attracting participants from industry, research, academia and end-users.
Internet address	https://warwick.ac.uk/fac/sci/dcs/people/chang-tsun_li/iwbf2017/ (Conference website)

Access to Document

10.1049/iet-bmt.2017.0151Licence: CC BY

23230282 Published paperFinal published version, 1.45 MBLicence: CC BY

Cite this

Jia, N., Sanchez, V., & Li, C. T. (2018). On view-invariant gait recognition: A feature selection solution. In M. Fairhurst (Ed.), IET biometrics: Special issue: Selected papers from the international workshop on biometrics and forensics (IWBF2017) (4 ed., Vol. 7, pp. 287-295). (IET Biometrics). Institution of Engineering and Technology (IET). https://doi.org/10.1049/iet-bmt.2017.0151

Jia, Ning ; Sanchez, Victor ; Li, Chang Tsun. / On view-invariant gait recognition : A feature selection solution. IET biometrics: Special issue: Selected papers from the international workshop on biometrics and forensics (IWBF2017). editor / Michael Fairhurst. Vol. 7 4. ed. United Kingdom : Institution of Engineering and Technology (IET), 2018. pp. 287-295 (IET Biometrics).

@inproceedings{7e8c39d98d604053aab658b6f647496a,

title = "On view-invariant gait recognition: A feature selection solution",

abstract = "The authors present an improved feature selection solution for the view-invariant gait recognition problem, based on their previously proposed method called view-invariant feature selector (ViFS), which automatically reconstruct an optimised gallery template from a set of multi-view gallery templates. They improved ViFS by introducing a constraint to make sure that the reconstructed features have the same scale as the original features, thus reducing the number of misclassifications caused by data misalignment. They evaluate the improved ViFS on the CASIA B and OU-ISIR large population datasets by performing a wide range of comparative studies in order to explore and confirm its effectiveness. Evaluation results indicate that the proposed framework is very effective for view-invariant gait recognition tasks.",

author = "Ning Jia and Victor Sanchez and Li, {Chang Tsun}",

note = "Includes bibliographical references.; 5th International Workshop on Biometrics and Forensics : IWBF 2017 ; Conference date: 04-04-2017 Through 05-04-2017",

year = "2018",

month = jun,

day = "25",

doi = "10.1049/iet-bmt.2017.0151",

language = "English",

volume = "7",

series = "IET Biometrics",

publisher = "Institution of Engineering and Technology (IET)",

pages = "287--295",

editor = "Michael Fairhurst",

booktitle = "IET biometrics",

edition = "4",

url = "https://warwick.ac.uk/fac/sci/dcs/people/chang-tsun_li/iwbf2017/",

}

Jia, N, Sanchez, V & Li, CT 2018, On view-invariant gait recognition: A feature selection solution. in M Fairhurst (ed.), IET biometrics: Special issue: Selected papers from the international workshop on biometrics and forensics (IWBF2017). 4 edn, vol. 7, IET Biometrics, Institution of Engineering and Technology (IET), United Kingdom, pp. 287-295, 5th International Workshop on Biometrics and Forensics, Coventry, United Kingdom, 04/04/17. https://doi.org/10.1049/iet-bmt.2017.0151

On view-invariant gait recognition: A feature selection solution. / Jia, Ning; Sanchez, Victor; Li, Chang Tsun.
IET biometrics: Special issue: Selected papers from the international workshop on biometrics and forensics (IWBF2017). ed. / Michael Fairhurst. Vol. 7 4. ed. United Kingdom: Institution of Engineering and Technology (IET), 2018. p. 287-295 (IET Biometrics).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - On view-invariant gait recognition

T2 - 5th International Workshop on Biometrics and Forensics

AU - Jia, Ning

AU - Sanchez, Victor

AU - Li, Chang Tsun

N1 - Includes bibliographical references.

PY - 2018/6/25

Y1 - 2018/6/25

N2 - The authors present an improved feature selection solution for the view-invariant gait recognition problem, based on their previously proposed method called view-invariant feature selector (ViFS), which automatically reconstruct an optimised gallery template from a set of multi-view gallery templates. They improved ViFS by introducing a constraint to make sure that the reconstructed features have the same scale as the original features, thus reducing the number of misclassifications caused by data misalignment. They evaluate the improved ViFS on the CASIA B and OU-ISIR large population datasets by performing a wide range of comparative studies in order to explore and confirm its effectiveness. Evaluation results indicate that the proposed framework is very effective for view-invariant gait recognition tasks.

AB - The authors present an improved feature selection solution for the view-invariant gait recognition problem, based on their previously proposed method called view-invariant feature selector (ViFS), which automatically reconstruct an optimised gallery template from a set of multi-view gallery templates. They improved ViFS by introducing a constraint to make sure that the reconstructed features have the same scale as the original features, thus reducing the number of misclassifications caused by data misalignment. They evaluate the improved ViFS on the CASIA B and OU-ISIR large population datasets by performing a wide range of comparative studies in order to explore and confirm its effectiveness. Evaluation results indicate that the proposed framework is very effective for view-invariant gait recognition tasks.

UR - http://www.scopus.com/inward/record.url?scp=85049120555&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049120555&partnerID=8YFLogxK

U2 - 10.1049/iet-bmt.2017.0151

DO - 10.1049/iet-bmt.2017.0151

M3 - Conference paper

AN - SCOPUS:85049120555

VL - 7

T3 - IET Biometrics

SP - 287

EP - 295

BT - IET biometrics

A2 - Fairhurst, Michael

PB - Institution of Engineering and Technology (IET)

CY - United Kingdom

Y2 - 4 April 2017 through 5 April 2017

ER -

Jia N, Sanchez V, Li CT. On view-invariant gait recognition: A feature selection solution. In Fairhurst M, editor, IET biometrics: Special issue: Selected papers from the international workshop on biometrics and forensics (IWBF2017). 4 ed. Vol. 7. United Kingdom: Institution of Engineering and Technology (IET). 2018. p. 287-295. (IET Biometrics). doi: 10.1049/iet-bmt.2017.0151

On view-invariant gait recognition: A feature selection solution

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this