Application of constrained local neural fields in face recognition

Jinna Sun; Mingting Yuan; Junhang Ding; Huasheng Xu; Qingmei Sui

doi:10.1109/CAC.2017.8244216

Application of constrained local neural fields in face recognition

Jinna Sun
, Mingting Yuan
, Junhang Ding
, Huasheng Xu
, Qingmei Sui

Qingdao University
Shandong University

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

Abstract

The facial feature points localization is the core of face recognition, and its accuracy directly affects the accuracy of face recognition system. The accuracy of facial feature points is affected by light, noise, background, and face gestures. Considering the theoretical value and practical significance of facial feature points localization, this thesis goes into the most advanced algorithm of facial feature points localization-Constrained Local Neural Fields. Taking into account the reliability of each patch model (feature point detector), CLNF combines the Local Neural Field patch model. The analysis contrasts the advantages of Constrained Local Models.

Original language	English
Title of host publication	Proceedings - 2017 Chinese Automation Congress, CAC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7920-7924
Number of pages	5
ISBN (Electronic)	9781538635247
DOIs	https://doi.org/10.1109/CAC.2017.8244216
State	Published - 29 Dec 2017
Externally published	Yes
Event	2017 Chinese Automation Congress, CAC 2017 - Jinan, China Duration: 20 Oct 2017 → 22 Oct 2017

Publication series

Name	Proceedings - 2017 Chinese Automation Congress, CAC 2017
Volume	2017-January

Conference

Conference	2017 Chinese Automation Congress, CAC 2017
Country/Territory	China
City	Jinan
Period	20/10/17 → 22/10/17

Keywords

Constrained Local Neural Fields
Facial feature points localization
Patch model

Access to Document

10.1109/CAC.2017.8244216

Cite this

Sun, J., Yuan, M., Ding, J., Xu, H., & Sui, Q. (2017). Application of constrained local neural fields in face recognition. In Proceedings - 2017 Chinese Automation Congress, CAC 2017 (pp. 7920-7924). (Proceedings - 2017 Chinese Automation Congress, CAC 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC.2017.8244216

@inproceedings{ea726199750440419372c8a67f8008f1,

title = "Application of constrained local neural fields in face recognition",

abstract = "The facial feature points localization is the core of face recognition, and its accuracy directly affects the accuracy of face recognition system. The accuracy of facial feature points is affected by light, noise, background, and face gestures. Considering the theoretical value and practical significance of facial feature points localization, this thesis goes into the most advanced algorithm of facial feature points localization-Constrained Local Neural Fields. Taking into account the reliability of each patch model (feature point detector), CLNF combines the Local Neural Field patch model. The analysis contrasts the advantages of Constrained Local Models.",

keywords = "Constrained Local Neural Fields, Facial feature points localization, Patch model",

author = "Jinna Sun and Mingting Yuan and Junhang Ding and Huasheng Xu and Qingmei Sui",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 Chinese Automation Congress, CAC 2017 ; Conference date: 20-10-2017 Through 22-10-2017",

year = "2017",

month = dec,

day = "29",

doi = "10.1109/CAC.2017.8244216",

language = "英语",

series = "Proceedings - 2017 Chinese Automation Congress, CAC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "7920--7924",

booktitle = "Proceedings - 2017 Chinese Automation Congress, CAC 2017",

}

Sun, J, Yuan, M, Ding, J, Xu, H & Sui, Q 2017, Application of constrained local neural fields in face recognition. in Proceedings - 2017 Chinese Automation Congress, CAC 2017. Proceedings - 2017 Chinese Automation Congress, CAC 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 7920-7924, 2017 Chinese Automation Congress, CAC 2017, Jinan, China, 20/10/17. https://doi.org/10.1109/CAC.2017.8244216

Application of constrained local neural fields in face recognition. / Sun, Jinna; Yuan, Mingting; Ding, Junhang et al.
Proceedings - 2017 Chinese Automation Congress, CAC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 7920-7924 (Proceedings - 2017 Chinese Automation Congress, CAC 2017; Vol. 2017-January).

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

TY - GEN

T1 - Application of constrained local neural fields in face recognition

AU - Sun, Jinna

AU - Yuan, Mingting

AU - Ding, Junhang

AU - Xu, Huasheng

AU - Sui, Qingmei

PY - 2017/12/29

Y1 - 2017/12/29

N2 - The facial feature points localization is the core of face recognition, and its accuracy directly affects the accuracy of face recognition system. The accuracy of facial feature points is affected by light, noise, background, and face gestures. Considering the theoretical value and practical significance of facial feature points localization, this thesis goes into the most advanced algorithm of facial feature points localization-Constrained Local Neural Fields. Taking into account the reliability of each patch model (feature point detector), CLNF combines the Local Neural Field patch model. The analysis contrasts the advantages of Constrained Local Models.

AB - The facial feature points localization is the core of face recognition, and its accuracy directly affects the accuracy of face recognition system. The accuracy of facial feature points is affected by light, noise, background, and face gestures. Considering the theoretical value and practical significance of facial feature points localization, this thesis goes into the most advanced algorithm of facial feature points localization-Constrained Local Neural Fields. Taking into account the reliability of each patch model (feature point detector), CLNF combines the Local Neural Field patch model. The analysis contrasts the advantages of Constrained Local Models.

KW - Constrained Local Neural Fields

KW - Facial feature points localization

KW - Patch model

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U2 - 10.1109/CAC.2017.8244216

DO - 10.1109/CAC.2017.8244216

M3 - 会议稿件

AN - SCOPUS:85050344474

T3 - Proceedings - 2017 Chinese Automation Congress, CAC 2017

SP - 7920

EP - 7924

BT - Proceedings - 2017 Chinese Automation Congress, CAC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 Chinese Automation Congress, CAC 2017

Y2 - 20 October 2017 through 22 October 2017

ER -

Application of constrained local neural fields in face recognition

Abstract

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Keywords

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