Face anti-spoofing algorithm based on semantic segmentation

被引：0

作者：

Lin Y. ^{[1
,2
]}

Sun X.-G. ^{[1
]}

Jiang Y.-G. ^{[1
,2
]}

Kang X. ^{[1
,2
]}

Xie Z.-X. ^{[1
,2
]}

Zhong Y. ^{[1
]}

机构：

[1] Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu

[2] University of Chinese Academy of Science, Beijing

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 03期

关键词：

Anti-spoofing; Computer application; Deep learning; Face recognition; Liveness detect; Picture fraud; Video fraud;

D O I：

10.13229/j.cnki.jdxbgxb20190074

中图分类号：

学科分类号：

摘要：

In order to improve the security of the face recognition system and prevent from fake face attack in mobile phones and photos, a face anti-spoofing algorithm based on semantic segmentation is proposed. First, the local face region is semantically segmented by full convolutional neural network (FCNN), and the convolution kernel with direction is proposed to optimize the network. Secondly, a fast classifier is trained to classify the results of the semantic segmentation network. Finally, the deep learning network is connected in series to form an end-to-end face anti-spoofing framework. The test results show that the performance of the proposed algorithm is outstanding in the Casia live dataset and private dataset collected by CBPM-XINDA, and the generalization ability in the actual project is outstanding. The main work includes the production of data sets and the selection of features (using the combination of deep learning and automatic learning features and manual selection of features), and proposes a method for optimizing convolution kernels and concatenation of multiple networks. © 2020, Jilin University Press. All right reserved.

引用

页码：1040 / 1046

页数：6

共 24 条

[1] Pan G., Sun L., Wu Z., Et al., Eyeblink-based anti- spoofing in face recognition from a generic webcamera, The 11th IEEE International Conference on Computer Vision, pp. 1-8, (2007)
[2] Zhang J., Zhang N.-N., Research on interactive face detection based on optimized feature extraction, Computer Engineering and Applications, 55, 13, pp. 193-200, (2019)
[3] Zhang Z., A flexible new technique for camera cali-bration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 11, pp. 1330-1334, (2000)
[4] Xiao Y.-S., Cao Y.-P., Wu Y.-C., Et al., A fast calibration method for the phase measuring profilometry, Journal of Sichuan University(Engineering Science Edition), 45, 1, pp. 164-168, (2013)
[5] Qiu Z.-Q., Three-dimensional reconstruction of face based on binocular stereoscopic vison, (2018)
[6] Yin W., Ming Y., Tian L., A face anti-spoofing method based on optical flow field, 13th International Conference on Signal Processing, pp. 1333-1337, (2016)
[7] Boulkenafet Z., Komulainen J., Hadid A., Face anti-spoofing based on color texture analysis, IEEE International Conference on Image Processing, (2015)
[8] Boulkenafet Z., Komulainen J., Hadid A., Face spoofing detection using colour texture analysis, IEEE Transactions on Information Forensics & Security, 11, 8, pp. 1818-1830, (2016)
[9] Atoum Y., Liu Y.J., Jourabloo A., Et al., Face anti-spoofing using patch and depth-based CNNs, IEEE International Joint Conference on Biometrics, (2017)
[10] Liu Y.J., Jourabloo A., Liu X.M., Learning deep models for face anti-spoofing: binary or auxiliary supervision

← 1 2 3 →