Gait recognition from incomplete gait cycle

Maryam Babaee; Linwei Li; Gerhard Rigoll

doi:10.1109/ICIP.2018.8451785

Gait recognition from incomplete gait cycle

Maryam Babaee, Linwei Li, Gerhard Rigoll

Chair of Human-Machine Communication

Technical University of Munich

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

29 Scopus citations

Abstract

In gait recognition, which has been recently regarded as a biometric recognition tool, proposed approaches assume that an individual is observed for at least one gait cycle. However, in reality, there might be available only a few frames of full gait cycle of a subject due to occlusion. Therefore, gait recognition systems would fail in these scenarios. In this paper, we propose a method to tackle this problem by proposing a gait recognition algorithm from an incomplete gait cycle information. We achieve this by 1) creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and 2) reconstructing the complete GEI from incomplete GEI using a deep auto-encoder. The experimental results on a public gait dataset demonstrate the validity of the proposed method.

Original language	English
Title of host publication	2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Publisher	IEEE Computer Society
Pages	768-772
Number of pages	5
ISBN (Electronic)	9781479970612
DOIs	https://doi.org/10.1109/ICIP.2018.8451785
State	Published - 29 Aug 2018
Event	25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece Duration: 7 Oct 2018 → 10 Oct 2018

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	25th IEEE International Conference on Image Processing, ICIP 2018
Country/Territory	Greece
City	Athens
Period	7/10/18 → 10/10/18

Keywords

Convolutional Neural Network
Gait Energy Image
Gait recognition

Access to Document

10.1109/ICIP.2018.8451785

Cite this

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title = "Gait recognition from incomplete gait cycle",

abstract = "In gait recognition, which has been recently regarded as a biometric recognition tool, proposed approaches assume that an individual is observed for at least one gait cycle. However, in reality, there might be available only a few frames of full gait cycle of a subject due to occlusion. Therefore, gait recognition systems would fail in these scenarios. In this paper, we propose a method to tackle this problem by proposing a gait recognition algorithm from an incomplete gait cycle information. We achieve this by 1) creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and 2) reconstructing the complete GEI from incomplete GEI using a deep auto-encoder. The experimental results on a public gait dataset demonstrate the validity of the proposed method.",

keywords = "Convolutional Neural Network, Gait Energy Image, Gait recognition",

author = "Maryam Babaee and Linwei Li and Gerhard Rigoll",

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year = "2018",

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doi = "10.1109/ICIP.2018.8451785",

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Babaee, M, Li, L & Rigoll, G 2018, Gait recognition from incomplete gait cycle. in 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings., 8451785, Proceedings - International Conference on Image Processing, ICIP, IEEE Computer Society, pp. 768-772, 25th IEEE International Conference on Image Processing, ICIP 2018, Athens, Greece, 7/10/18. https://doi.org/10.1109/ICIP.2018.8451785

Gait recognition from incomplete gait cycle. / Babaee, Maryam; Li, Linwei; Rigoll, Gerhard.
2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings. IEEE Computer Society, 2018. p. 768-772 8451785 (Proceedings - International Conference on Image Processing, ICIP).

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

TY - GEN

T1 - Gait recognition from incomplete gait cycle

AU - Babaee, Maryam

AU - Li, Linwei

AU - Rigoll, Gerhard

PY - 2018/8/29

Y1 - 2018/8/29

N2 - In gait recognition, which has been recently regarded as a biometric recognition tool, proposed approaches assume that an individual is observed for at least one gait cycle. However, in reality, there might be available only a few frames of full gait cycle of a subject due to occlusion. Therefore, gait recognition systems would fail in these scenarios. In this paper, we propose a method to tackle this problem by proposing a gait recognition algorithm from an incomplete gait cycle information. We achieve this by 1) creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and 2) reconstructing the complete GEI from incomplete GEI using a deep auto-encoder. The experimental results on a public gait dataset demonstrate the validity of the proposed method.

AB - In gait recognition, which has been recently regarded as a biometric recognition tool, proposed approaches assume that an individual is observed for at least one gait cycle. However, in reality, there might be available only a few frames of full gait cycle of a subject due to occlusion. Therefore, gait recognition systems would fail in these scenarios. In this paper, we propose a method to tackle this problem by proposing a gait recognition algorithm from an incomplete gait cycle information. We achieve this by 1) creating an incomplete Energy Image (GEI) from a few available silhouettes of a subject and 2) reconstructing the complete GEI from incomplete GEI using a deep auto-encoder. The experimental results on a public gait dataset demonstrate the validity of the proposed method.

KW - Convolutional Neural Network

KW - Gait Energy Image

KW - Gait recognition

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DO - 10.1109/ICIP.2018.8451785

M3 - Conference contribution

AN - SCOPUS:85062896270

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 768

EP - 772

BT - 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings

PB - IEEE Computer Society

T2 - 25th IEEE International Conference on Image Processing, ICIP 2018

Y2 - 7 October 2018 through 10 October 2018

ER -

Gait recognition from incomplete gait cycle

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Keywords

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