@inproceedings{58088acc7a5e4aadbd5d3f966b82c243,
title = "Fusing multiple deep features for face anti-spoofing",
abstract = "With the growing deployment of face recognition system in recent years, face anti-spoofing has become increasingly important, due to the increasing number of spoofing attacks via printed photos or replayed videos. Motivated by the powerful representation ability of deep learning, in this paper we propose to use CNNs (Convolutional Neural Networks) to learn multiple deep features from different cues of the face images for anti-spoofing. We integrate temporal features, color based features and patch based local features for spoof detection. We evaluate our approach extensively on publicly available databases like CASIA FASD, REPLAY-MOBILE and OULU-NPU. The experimental results show that our approach can achieve much better performance than state-of-the-art methods. Specifically, 2.22% of EER (Equal Error Rate) on the CASIA FASD, 3.2% of ACER (Average Classification Error Rate) on the OULU-NPU (protocol 1) and 0.00% of ACER on the REPLAY-MOBILE database are achieved.",
keywords = "Deep convolutional neural networks, Face anti-spoofing, Multiple features",
author = "Yan Tang and Xing Wang and Xi Jia and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 13th Chinese Conference on Biometric Recognition, CCBR 2018 ; Conference date: 11-08-2018 Through 12-08-2018",
year = "2018",
doi = "10.1007/978-3-319-97909-0_35",
language = "English",
isbn = "9783319979083",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "321--330",
editor = "Zhenan Sun and Shiguang Shan and Zhenhong Jia and Kurban Ubul and Jie Zhou and Jianjiang Feng and Zhenhua Guo and Yunhong Wang",
booktitle = "Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings",
address = "Germany",
}