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Gait Recognition: Databases, Representations, and Applications

Gait Recognition: Databases, Representations, and Applications
Gait Recognition: Databases, Representations, and Applications
There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.
Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.
Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.
Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition.
Wiley
Makihara, Yasushi
d1e46e57-180f-448c-8510-b957b9471ce0
Matovski, Darko
33c2d81d-3a4e-4163-814e-513d4f09ae5b
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Yagi, Yasushi
45fe32b7-b5bd-479a-854c-c4060b8e6921
Makihara, Yasushi
d1e46e57-180f-448c-8510-b957b9471ce0
Matovski, Darko
33c2d81d-3a4e-4163-814e-513d4f09ae5b
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Yagi, Yasushi
45fe32b7-b5bd-479a-854c-c4060b8e6921

Makihara, Yasushi, Matovski, Darko, Nixon, M.S., Carter, John N. and Yagi, Yasushi (2015) Gait Recognition: Databases, Representations, and Applications. In, Encyclopedia of Electrical and Electronics Engineering. Wiley.

Record type: Book Section

Abstract

There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a person’s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.
Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.
Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.
Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition.

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Accepted/In Press date: February 2015
Published date: 15 June 2015
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 376425
URI: http://eprints.soton.ac.uk/id/eprint/376425
PURE UUID: d923eaa4-ec00-4aae-8bff-85d18334ef09
ORCID for M.S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 21 Apr 2015 11:20
Last modified: 06 Jun 2018 13:18

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Contributors

Author: Yasushi Makihara
Author: Darko Matovski
Author: M.S. Nixon ORCID iD
Author: John N. Carter
Author: Yasushi Yagi

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