3D aided face recognition across pose variations

Wuming Zhang; Di Huang; Yunhong Wang; Liming Chen

doi:10.1007/978-3-642-35136-5_8

3D aided face recognition across pose variations

Wuming Zhang^*
, Di Huang
, Yunhong Wang
, Liming Chen

^*Corresponding author for this work

School of Computer Science and Engineering

École centrale de Lyon
Beihang University

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

10 Scopus citations

Abstract

Recently, 3D aided face recognition, concentrating on improving performance of 2D techniques via 3D data, has received increasing attention due to its wide application potential in real condition. In this paper, we present a novel 3D aided face recognition method that can deal with the probe images in different viewpoints. It first estimates the face pose based on the Random Regression Forest, and then rotates the 3D face models in the gallery set to that of the probe pose to generate specific gallery sample for matching, which largely reduces the influence of head pose variations. Experiments are carried out on a subset of the FRGC v1.0 database, and the achieved performance clearly highlights the effectiveness of the proposed method.

Original language	English
Title of host publication	Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings
Pages	58-66
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-35136-5_8
State	Published - 2012
Event	7th Chinese Conference on Biometric Recognition, CCBR 2012 - Guangzhou, China Duration: 4 Dec 2012 → 5 Dec 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7701 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th Chinese Conference on Biometric Recognition, CCBR 2012
Country/Territory	China
City	Guangzhou
Period	4/12/12 → 5/12/12

Keywords

Face recognition
LBP
pose estimation
random regression forests

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10.1007/978-3-642-35136-5_8

Cite this

Zhang, W., Huang, D., Wang, Y., & Chen, L. (2012). 3D aided face recognition across pose variations. In Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings (pp. 58-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7701 LNCS). https://doi.org/10.1007/978-3-642-35136-5_8

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title = "3D aided face recognition across pose variations",

abstract = "Recently, 3D aided face recognition, concentrating on improving performance of 2D techniques via 3D data, has received increasing attention due to its wide application potential in real condition. In this paper, we present a novel 3D aided face recognition method that can deal with the probe images in different viewpoints. It first estimates the face pose based on the Random Regression Forest, and then rotates the 3D face models in the gallery set to that of the probe pose to generate specific gallery sample for matching, which largely reduces the influence of head pose variations. Experiments are carried out on a subset of the FRGC v1.0 database, and the achieved performance clearly highlights the effectiveness of the proposed method.",

keywords = "Face recognition, LBP, pose estimation, random regression forests",

author = "Wuming Zhang and Di Huang and Yunhong Wang and Liming Chen",

year = "2012",

doi = "10.1007/978-3-642-35136-5\_8",

language = "英语",

isbn = "9783642355059",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "58--66",

booktitle = "Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings",

note = "7th Chinese Conference on Biometric Recognition, CCBR 2012 ; Conference date: 04-12-2012 Through 05-12-2012",

}

Zhang, W, Huang, D , Wang, Y & Chen, L 2012, 3D aided face recognition across pose variations. in Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7701 LNCS, pp. 58-66, 7th Chinese Conference on Biometric Recognition, CCBR 2012, Guangzhou, China, 4/12/12. https://doi.org/10.1007/978-3-642-35136-5_8

3D aided face recognition across pose variations. / Zhang, Wuming; Huang, Di ; Wang, Yunhong et al.
Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings. 2012. p. 58-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7701 LNCS).

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

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AU - Zhang, Wuming

AU - Huang, Di

AU - Wang, Yunhong

AU - Chen, Liming

PY - 2012

Y1 - 2012

N2 - Recently, 3D aided face recognition, concentrating on improving performance of 2D techniques via 3D data, has received increasing attention due to its wide application potential in real condition. In this paper, we present a novel 3D aided face recognition method that can deal with the probe images in different viewpoints. It first estimates the face pose based on the Random Regression Forest, and then rotates the 3D face models in the gallery set to that of the probe pose to generate specific gallery sample for matching, which largely reduces the influence of head pose variations. Experiments are carried out on a subset of the FRGC v1.0 database, and the achieved performance clearly highlights the effectiveness of the proposed method.

AB - Recently, 3D aided face recognition, concentrating on improving performance of 2D techniques via 3D data, has received increasing attention due to its wide application potential in real condition. In this paper, we present a novel 3D aided face recognition method that can deal with the probe images in different viewpoints. It first estimates the face pose based on the Random Regression Forest, and then rotates the 3D face models in the gallery set to that of the probe pose to generate specific gallery sample for matching, which largely reduces the influence of head pose variations. Experiments are carried out on a subset of the FRGC v1.0 database, and the achieved performance clearly highlights the effectiveness of the proposed method.

KW - Face recognition

KW - LBP

KW - pose estimation

KW - random regression forests

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DO - 10.1007/978-3-642-35136-5_8

M3 - 会议稿件

AN - SCOPUS:84871381431

SN - 9783642355059

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 66

BT - Biometric Recognition - 7th Chinese Conference, CCBR 2012, Proceedings

T2 - 7th Chinese Conference on Biometric Recognition, CCBR 2012

Y2 - 4 December 2012 through 5 December 2012

ER -

3D aided face recognition across pose variations

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