TY - GEN
T1 - Face Anti-spoofing Based on Client Identity Information and Depth Map
AU - Wang, Yu
AU - Pei, Mingtao
AU - Nie, Zhengang
AU - Qi, Xinmu
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Face anti-spoofing (FAS) is an essential prerequisite for face recognition. In most methods, FAS is usually performed before face recognition and the client identity information is not utilized. Since presentation attacks (PAs) are always aimed at a certain client, the client identity information can provide useful clues for FAS task. In this paper, we propose a face anti-spoofing method based on client identity information using Siamese network. We applied FAS after face recognition to utilize the client identity information. As the real face and fake face have different properties, we use different weights for the two subnetworks of the Siamese network to extract features for real face and fake face, respectively. In addition, we employ depth map as auxiliary information to improve the performance. We perform experiments on SiW, CASIA-FASD and Replay-Attack datasets to demonstrate the validity of our method.
AB - Face anti-spoofing (FAS) is an essential prerequisite for face recognition. In most methods, FAS is usually performed before face recognition and the client identity information is not utilized. Since presentation attacks (PAs) are always aimed at a certain client, the client identity information can provide useful clues for FAS task. In this paper, we propose a face anti-spoofing method based on client identity information using Siamese network. We applied FAS after face recognition to utilize the client identity information. As the real face and fake face have different properties, we use different weights for the two subnetworks of the Siamese network to extract features for real face and fake face, respectively. In addition, we employ depth map as auxiliary information to improve the performance. We perform experiments on SiW, CASIA-FASD and Replay-Attack datasets to demonstrate the validity of our method.
KW - Client identity information
KW - Depth map
KW - Face anti-spoofing
KW - Siamese network
UR - http://www.scopus.com/inward/record.url?scp=85177427314&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-46305-1_31
DO - 10.1007/978-3-031-46305-1_31
M3 - Conference contribution
AN - SCOPUS:85177427314
SN - 9783031463044
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 380
EP - 389
BT - Image and Graphics - 12th International Conference, ICIG 2023, Proceedings
A2 - Lu, Huchuan
A2 - Liu, Risheng
A2 - Ouyang, Wanli
A2 - Huang, Hui
A2 - Lu, Jiwen
A2 - Dong, Jing
A2 - Xu, Min
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Image and Graphics, ICIG 2023
Y2 - 22 September 2023 through 24 September 2023
ER -