@inproceedings{2139a29c712f42948506596d4ab7e547,
title = "Domain adaptation based on ResADDA model for face anti-spoofing detection",
abstract = "Different datasets have more apparent differences due to lighting, background and image quality issues, which makes the generalization problem of face anti-spoofing detection more prominent. A domain adaptive method for face spoofing detection based on ResADDA model is proposed, which adopts the ResNet34 network to extract deep convolutional features, and draws on the GAN network idea to use adversarial training by alternately optimizing the domain discriminator and feature encoder, adjusting the parameters of the target domain feature encoder and reducing the difference of feature distribution between the target domain and the source domain to improve the detection ability of the model on the target domain. Crossover experiments on the publicly available dataset CASIA-FASD and Replay-Attack are conducted to verify the effectiveness of the ResADDA model which is superior to other methods.",
keywords = "Adversarial discriminative, Domain adaptation, Face anti-spoofing detection, Residual network",
author = "Feng Jun and Dong Zhiyi and Shi Yichen and Hu Jingjing",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2021 ; Conference date: 27-08-2021 Through 29-08-2021",
year = "2021",
month = aug,
doi = "10.1109/ICCEAI52939.2021.00059",
language = "English",
series = "Proceedings - 2021 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "295--299",
editor = "Pan Lin and Yong Yang",
booktitle = "Proceedings - 2021 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2021",
address = "United States",
}