SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

Meiling Fang; Marco Huber; Julian Fierrez; Raghavendra Ramachandra; Naser Damer; Alhasan Alkhaddour; Maksim Kasantcev; Vasiliy Pryadchenko; Ziyuan Yang; Huijie Huangfu; Yingyu Chen; Yi Zhang; Yuchen Pan; Junjun Jiang; Xianming Liu; Xianyun Sun; Caiyong Wang; Xingyu Liu; Zhaohua Chang; Guangzhe Zhao; Juan Tapia; Lazaro Gonzalez-Soler; Carlos Aravena; Daniel Schulz

doi:10.1109/IJCB57857.2023.10449130

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

Meiling Fang^*
, Marco Huber
, Julian Fierrez
, Raghavendra Ramachandra
, Naser Damer
, Alhasan Alkhaddour
, Maksim Kasantcev
, Vasiliy Pryadchenko
, Ziyuan Yang
, Huijie Huangfu
, Yingyu Chen
, Yi Zhang
, Yuchen Pan
, Junjun Jiang
, Xianming Liu
, Xianyun Sun
, Caiyong Wang
, Xingyu Liu
, Zhaohua Chang
, Guangzhe Zhao

Juan Tapia, Lazaro Gonzalez-Soler, Carlos Aravena, Daniel Schulz

^*Corresponding author for this work

Fraunhofer Institute for Computer Graphics Research
Technische Universität Darmstadt
Universidad Autónoma de Madrid
Norwegian University of Science and Technology
Inc
School of Cyber Science and Engineering
School of Computer Science and Technology, Harbin Institute of Technology
Beijing University of Civil Engineering and Architecture
Biometrics and Security Research Group
I+D Vision Center

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

9 Scopus citations

Abstract

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

Original language	English
Title of host publication	2023 IEEE International Joint Conference on Biometrics, IJCB 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350337266
DOIs	https://doi.org/10.1109/IJCB57857.2023.10449130
State	Published - 2023
Externally published	Yes
Event	2023 IEEE International Joint Conference on Biometrics, IJCB 2023 - Ljubljana, Slovenia Duration: 25 Sep 2023 → 28 Sep 2023

Publication series

Name	2023 IEEE International Joint Conference on Biometrics, IJCB 2023

Conference

Conference	2023 IEEE International Joint Conference on Biometrics, IJCB 2023
Country/Territory	Slovenia
City	Ljubljana
Period	25/09/23 → 28/09/23

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10.1109/IJCB57857.2023.10449130

Cite this

Fang, M., Huber, M., Fierrez, J., Ramachandra, R., Damer, N., Alkhaddour, A., Kasantcev, M., Pryadchenko, V., Yang, Z., Huangfu, H., Chen, Y., Zhang, Y., Pan, Y., Jiang, J., Liu, X., Sun, X., Wang, C., Liu, X., Chang, Z., ... Schulz, D. (2023). SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data. In 2023 IEEE International Joint Conference on Biometrics, IJCB 2023 (2023 IEEE International Joint Conference on Biometrics, IJCB 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCB57857.2023.10449130

@inproceedings{850ff2e1b5974e4f84cea1148d454aad,

title = "SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data",

abstract = "This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.",

author = "Meiling Fang and Marco Huber and Julian Fierrez and Raghavendra Ramachandra and Naser Damer and Alhasan Alkhaddour and Maksim Kasantcev and Vasiliy Pryadchenko and Ziyuan Yang and Huijie Huangfu and Yingyu Chen and Yi Zhang and Yuchen Pan and Junjun Jiang and Xianming Liu and Xianyun Sun and Caiyong Wang and Xingyu Liu and Zhaohua Chang and Guangzhe Zhao and Juan Tapia and Lazaro Gonzalez-Soler and Carlos Aravena and Daniel Schulz",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Joint Conference on Biometrics, IJCB 2023 ; Conference date: 25-09-2023 Through 28-09-2023",

year = "2023",

doi = "10.1109/IJCB57857.2023.10449130",

language = "英语",

series = "2023 IEEE International Joint Conference on Biometrics, IJCB 2023",

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

booktitle = "2023 IEEE International Joint Conference on Biometrics, IJCB 2023",

address = "美国",

}

Fang, M, Huber, M, Fierrez, J, Ramachandra, R, Damer, N, Alkhaddour, A, Kasantcev, M, Pryadchenko, V, Yang, Z, Huangfu, H, Chen, Y, Zhang, Y, Pan, Y, Jiang, J , Liu, X, Sun, X, Wang, C, Liu, X, Chang, Z, Zhao, G, Tapia, J, Gonzalez-Soler, L, Aravena, C & Schulz, D 2023, SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data. in 2023 IEEE International Joint Conference on Biometrics, IJCB 2023. 2023 IEEE International Joint Conference on Biometrics, IJCB 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Joint Conference on Biometrics, IJCB 2023, Ljubljana, Slovenia, 25/09/23. https://doi.org/10.1109/IJCB57857.2023.10449130

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data. / Fang, Meiling; Huber, Marco; Fierrez, Julian et al.
2023 IEEE International Joint Conference on Biometrics, IJCB 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Joint Conference on Biometrics, IJCB 2023).

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

TY - GEN

T1 - SynFacePAD 2023

T2 - 2023 IEEE International Joint Conference on Biometrics, IJCB 2023

AU - Fang, Meiling

AU - Huber, Marco

AU - Fierrez, Julian

AU - Ramachandra, Raghavendra

AU - Damer, Naser

AU - Alkhaddour, Alhasan

AU - Kasantcev, Maksim

AU - Pryadchenko, Vasiliy

AU - Yang, Ziyuan

AU - Huangfu, Huijie

AU - Chen, Yingyu

AU - Zhang, Yi

AU - Pan, Yuchen

AU - Jiang, Junjun

AU - Liu, Xianming

AU - Sun, Xianyun

AU - Wang, Caiyong

AU - Liu, Xingyu

AU - Chang, Zhaohua

AU - Zhao, Guangzhe

AU - Tapia, Juan

AU - Gonzalez-Soler, Lazaro

AU - Aravena, Carlos

AU - Schulz, Daniel

PY - 2023

Y1 - 2023

N2 - This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

AB - This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

UR - https://www.scopus.com/pages/publications/85187540659

U2 - 10.1109/IJCB57857.2023.10449130

DO - 10.1109/IJCB57857.2023.10449130

M3 - 会议稿件

AN - SCOPUS:85187540659

T3 - 2023 IEEE International Joint Conference on Biometrics, IJCB 2023

BT - 2023 IEEE International Joint Conference on Biometrics, IJCB 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 September 2023 through 28 September 2023

ER -

Fang M, Huber M, Fierrez J, Ramachandra R, Damer N, Alkhaddour A et al. SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data. In 2023 IEEE International Joint Conference on Biometrics, IJCB 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Joint Conference on Biometrics, IJCB 2023). doi: 10.1109/IJCB57857.2023.10449130

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

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