Attributes preserving face de-identification

Bin Yan; Mingtao Pei; Zhengang Nie

doi:10.1109/ICCVW.2019.00154

Attributes preserving face de-identification

Bin Yan, Mingtao Pei, Zhengang Nie

Beijing Institute of Technology

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

7 Citations (Scopus)

Abstract

In this paper, we propose a Face de-identification method to remove the identification information of a person while maintaining all the face attributes such as expression, age and gender. Motivated by the k-Same algorithm, our method consists of three steps: first, k face images are selected randomly. These k face images may contain same or different face attributes with the test face image. Secondly, ELEGANT model is employed to transfer attributes from the test face to the k selected faces. After attributes transferring, the k selected faces have the same attributes as the test face. Then we average the k selected faces as the de-identified image of the test face. Experimental results show that our method can de-identify a face image while preserving all of its attributes effectively.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1217-1221
Number of pages	5
ISBN (Electronic)	9781728150239
DOIs	https://doi.org/10.1109/ICCVW.2019.00154
Publication status	Published - Oct 2019
Event	17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of Duration: 27 Oct 2019 → 28 Oct 2019

Publication series

Name	Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

Conference

Conference	17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
Country/Territory	Korea, Republic of
City	Seoul
Period	27/10/19 → 28/10/19

Keywords

ELEGANT model
Face de identification
K same algorithm

Access to Document

10.1109/ICCVW.2019.00154

Cite this

Yan, B., Pei, M., & Nie, Z. (2019). Attributes preserving face de-identification. In Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 (pp. 1217-1221). Article 9022403 (Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2019.00154

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title = "Attributes preserving face de-identification",

abstract = "In this paper, we propose a Face de-identification method to remove the identification information of a person while maintaining all the face attributes such as expression, age and gender. Motivated by the k-Same algorithm, our method consists of three steps: first, k face images are selected randomly. These k face images may contain same or different face attributes with the test face image. Secondly, ELEGANT model is employed to transfer attributes from the test face to the k selected faces. After attributes transferring, the k selected faces have the same attributes as the test face. Then we average the k selected faces as the de-identified image of the test face. Experimental results show that our method can de-identify a face image while preserving all of its attributes effectively.",

keywords = "ELEGANT model, Face de identification, K same algorithm",

author = "Bin Yan and Mingtao Pei and Zhengang Nie",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 ; Conference date: 27-10-2019 Through 28-10-2019",

year = "2019",

month = oct,

doi = "10.1109/ICCVW.2019.00154",

language = "English",

series = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",

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

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booktitle = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",

address = "United States",

}

Yan, B, Pei, M & Nie, Z 2019, Attributes preserving face de-identification. in Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019., 9022403, Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1217-1221, 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019, Seoul, Korea, Republic of, 27/10/19. https://doi.org/10.1109/ICCVW.2019.00154

Attributes preserving face de-identification. / Yan, Bin; Pei, Mingtao; Nie, Zhengang.
Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1217-1221 9022403 (Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019).

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

TY - GEN

T1 - Attributes preserving face de-identification

AU - Yan, Bin

AU - Pei, Mingtao

AU - Nie, Zhengang

PY - 2019/10

Y1 - 2019/10

N2 - In this paper, we propose a Face de-identification method to remove the identification information of a person while maintaining all the face attributes such as expression, age and gender. Motivated by the k-Same algorithm, our method consists of three steps: first, k face images are selected randomly. These k face images may contain same or different face attributes with the test face image. Secondly, ELEGANT model is employed to transfer attributes from the test face to the k selected faces. After attributes transferring, the k selected faces have the same attributes as the test face. Then we average the k selected faces as the de-identified image of the test face. Experimental results show that our method can de-identify a face image while preserving all of its attributes effectively.

AB - In this paper, we propose a Face de-identification method to remove the identification information of a person while maintaining all the face attributes such as expression, age and gender. Motivated by the k-Same algorithm, our method consists of three steps: first, k face images are selected randomly. These k face images may contain same or different face attributes with the test face image. Secondly, ELEGANT model is employed to transfer attributes from the test face to the k selected faces. After attributes transferring, the k selected faces have the same attributes as the test face. Then we average the k selected faces as the de-identified image of the test face. Experimental results show that our method can de-identify a face image while preserving all of its attributes effectively.

KW - ELEGANT model

KW - Face de identification

KW - K same algorithm

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M3 - Conference contribution

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T3 - Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

SP - 1217

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BT - Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019

Y2 - 27 October 2019 through 28 October 2019

ER -

Attributes preserving face de-identification

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Keywords

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