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