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Large Crowdcollected Facial Anti-Spoofing Dataset. / Timoshenko, Denis; Simonchik, Konstantin; Shutov, Vitaly; Zhelezneva, Polina; Grishkin, Valery.

12th International Conference on Computer Science and Information Technologies, CSIT 2019. ed. / Samvel Shoukourian. Institute of Electrical and Electronics Engineers Inc., 2019. p. 123-126 8895208 (12th International Conference on Computer Science and Information Technologies, CSIT 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Timoshenko, D, Simonchik, K, Shutov, V, Zhelezneva, P & Grishkin, V 2019, Large Crowdcollected Facial Anti-Spoofing Dataset. in S Shoukourian (ed.), 12th International Conference on Computer Science and Information Technologies, CSIT 2019., 8895208, 12th International Conference on Computer Science and Information Technologies, CSIT 2019, Institute of Electrical and Electronics Engineers Inc., pp. 123-126, 12th International Conference on Computer Science and Information Technologies, CSIT 2019, Yerevan, Armenia, 23/09/19. https://doi.org/10.1109/CSITechnol.2019.8895208

APA

Timoshenko, D., Simonchik, K., Shutov, V., Zhelezneva, P., & Grishkin, V. (2019). Large Crowdcollected Facial Anti-Spoofing Dataset. In S. Shoukourian (Ed.), 12th International Conference on Computer Science and Information Technologies, CSIT 2019 (pp. 123-126). [8895208] (12th International Conference on Computer Science and Information Technologies, CSIT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSITechnol.2019.8895208

Vancouver

Timoshenko D, Simonchik K, Shutov V, Zhelezneva P, Grishkin V. Large Crowdcollected Facial Anti-Spoofing Dataset. In Shoukourian S, editor, 12th International Conference on Computer Science and Information Technologies, CSIT 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 123-126. 8895208. (12th International Conference on Computer Science and Information Technologies, CSIT 2019). https://doi.org/10.1109/CSITechnol.2019.8895208

Author

Timoshenko, Denis ; Simonchik, Konstantin ; Shutov, Vitaly ; Zhelezneva, Polina ; Grishkin, Valery. / Large Crowdcollected Facial Anti-Spoofing Dataset. 12th International Conference on Computer Science and Information Technologies, CSIT 2019. editor / Samvel Shoukourian. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 123-126 (12th International Conference on Computer Science and Information Technologies, CSIT 2019).

BibTeX

@inproceedings{cffd66d56d0b4b8a814b45334cbd8b33,
title = "Large Crowdcollected Facial Anti-Spoofing Dataset",
abstract = "The study about the vulnerabilities of biometric systems against spoofing has been a very active field of research in recent years. In this particular research we are focusing on one of the most difficult types of attack - video replay. We have noticed that currently most of face replay anti-spoofing databases focus on data with little variations of the devices used for replay and record. This fact may limit the generalization performance of trained models since potential attacks in the real world are probably more complex. In this review we present a face anti-spoofing database, which covers a huge range of different devices used for recording and for the video playback. The database contains 1942 genuine images, and 16885 fake faces are made from high quality records of the genuine faces. The database was collected using Amazon Mechanical Turk and Yandex Toloka services. The database was manually checked and the test protocol was provided. Some methods are also provided to be used as a baseline for future research. We hope that database as such can serve as an evaluation platform for the future studies in the literature.",
keywords = "biometrics, computer science, datasets",
author = "Denis Timoshenko and Konstantin Simonchik and Vitaly Shutov and Polina Zhelezneva and Valery Grishkin",
year = "2019",
month = sep,
doi = "10.1109/CSITechnol.2019.8895208",
language = "English",
series = "12th International Conference on Computer Science and Information Technologies, CSIT 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "123--126",
editor = "Samvel Shoukourian",
booktitle = "12th International Conference on Computer Science and Information Technologies, CSIT 2019",
address = "United States",
note = "12th International Conference on Computer Science and Information Technologies, CSIT 2019 ; Conference date: 23-09-2019 Through 27-09-2019",

}

RIS

TY - GEN

T1 - Large Crowdcollected Facial Anti-Spoofing Dataset

AU - Timoshenko, Denis

AU - Simonchik, Konstantin

AU - Shutov, Vitaly

AU - Zhelezneva, Polina

AU - Grishkin, Valery

PY - 2019/9

Y1 - 2019/9

N2 - The study about the vulnerabilities of biometric systems against spoofing has been a very active field of research in recent years. In this particular research we are focusing on one of the most difficult types of attack - video replay. We have noticed that currently most of face replay anti-spoofing databases focus on data with little variations of the devices used for replay and record. This fact may limit the generalization performance of trained models since potential attacks in the real world are probably more complex. In this review we present a face anti-spoofing database, which covers a huge range of different devices used for recording and for the video playback. The database contains 1942 genuine images, and 16885 fake faces are made from high quality records of the genuine faces. The database was collected using Amazon Mechanical Turk and Yandex Toloka services. The database was manually checked and the test protocol was provided. Some methods are also provided to be used as a baseline for future research. We hope that database as such can serve as an evaluation platform for the future studies in the literature.

AB - The study about the vulnerabilities of biometric systems against spoofing has been a very active field of research in recent years. In this particular research we are focusing on one of the most difficult types of attack - video replay. We have noticed that currently most of face replay anti-spoofing databases focus on data with little variations of the devices used for replay and record. This fact may limit the generalization performance of trained models since potential attacks in the real world are probably more complex. In this review we present a face anti-spoofing database, which covers a huge range of different devices used for recording and for the video playback. The database contains 1942 genuine images, and 16885 fake faces are made from high quality records of the genuine faces. The database was collected using Amazon Mechanical Turk and Yandex Toloka services. The database was manually checked and the test protocol was provided. Some methods are also provided to be used as a baseline for future research. We hope that database as such can serve as an evaluation platform for the future studies in the literature.

KW - biometrics

KW - computer science

KW - datasets

UR - http://www.scopus.com/inward/record.url?scp=85075635335&partnerID=8YFLogxK

U2 - 10.1109/CSITechnol.2019.8895208

DO - 10.1109/CSITechnol.2019.8895208

M3 - Conference contribution

T3 - 12th International Conference on Computer Science and Information Technologies, CSIT 2019

SP - 123

EP - 126

BT - 12th International Conference on Computer Science and Information Technologies, CSIT 2019

A2 - Shoukourian, Samvel

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th International Conference on Computer Science and Information Technologies, CSIT 2019

Y2 - 23 September 2019 through 27 September 2019

ER -

ID: 51584197