TY - JOUR

T1 - Application of Convolutional Neural Networks for Face Anstispoofing

AU - Pakulich, D. V.

AU - Alyamkin, S. A.

N1 - Publisher Copyright: © 2021, Allerton Press, Inc.

PY - 2021/7

Y1 - 2021/7

N2 - An increase in the share of computer vision for biometric identification systems and its application as a security measure leads to the growth of falsification attempts. In view of this fact, the number of methods for the automatic detection of such situations also grows. However, similarly to most of the systems using computer vision in different situations, the attack detection precision may decrease in some cases. The submitted paper considers the existing approaches to the detection of spoofing attacks and gives an estimate for their stability under changing date recording conditions.

AB - An increase in the share of computer vision for biometric identification systems and its application as a security measure leads to the growth of falsification attempts. In view of this fact, the number of methods for the automatic detection of such situations also grows. However, similarly to most of the systems using computer vision in different situations, the attack detection precision may decrease in some cases. The submitted paper considers the existing approaches to the detection of spoofing attacks and gives an estimate for their stability under changing date recording conditions.

KW - computer vision

KW - convolutional neural networks

KW - deep neural networks

KW - detection of spoofing attacks

UR - http://www.scopus.com/inward/record.url?scp=85122918327&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/48a271e4-f286-324e-9aff-79aa95734256/

U2 - 10.3103/S8756699021040099

DO - 10.3103/S8756699021040099

M3 - Article

AN - SCOPUS:85122918327

VL - 57

SP - 412

EP - 418

JO - Optoelectronics, Instrumentation and Data Processing

JF - Optoelectronics, Instrumentation and Data Processing

SN - 8756-6990

IS - 4

M1 - 11

ER -