Face Anti-Spoofing Algorithm Using Generative Adversarial Networks with Hypercomplex Wavelet

doi:10.7652/xjtuxb202105013

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Face Anti-Spoofing Algorithm Using Generative Adversarial Networks with Hypercomplex Wavelet

更新时间：2025-07-09

- Face Anti-Spoofing Algorithm Using Generative Adversarial Networks with Hypercomplex Wavelet
- Vol. 55, Issue 5, Pages: 113-122(2021)
- 作者机构：
  
  兰州理工大学电气工程与信息工程学院,兰州,730050
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202105013
  CLC： TP751.1
- Online First：10 May 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
Face Anti-Spoofing Algorithm Using Generative Adversarial Networks with Hypercomplex Wavelet[J]. 2021, 55(5): 113-122.
DOI：

Face Anti-Spoofing Algorithm Using Generative Adversarial Networks with Hypercomplex Wavelet[J]. 2021, 55(5): 113-122. DOI： 10.7652/xjtuxb202105013.

摘要

为了提升人脸识别系统判别图像真实性的能力

针对较难检测到未知的活体人脸攻击问题

提出了一种采用超复数小波生成对抗网络的活体人脸检测算法。采用4个不同类型的数据集

随机选择3个作为训练集

另一个作为测试集

形成训练时未知的活体人脸。训练集视为3个源域

输入到超复数小波生成对抗网络中

使一个特征生成器与3个判别器进行对抗

当特征生成器成功欺骗过3个判别器时

形成具有3个源域共享且区别于3个源域的特征空间

能够检测到不同于源域的人脸特征。在判别器上设置了域间和域内的三元组约束函数

以此提高判别器的性能

将超复数小波的细节子带图与卷积网络联合

学习图像多个方向的细节纹理特征

用来提升判别器鉴定活性人脸特征的能力。由于真假人脸的远程光电体积描记术和深度图都具有较大的差异

所以将其嵌入到特征空间中

增强生成特征空间检测人脸特征的泛化性能

形成通用的特征空间。在该特征空间中使用测试集进行判别分类

得到真假人脸识别结果。实验结果表明

在CASIA-FASD、Replay-Attack和NUAA数据集上

所提算法的接受者操作特性曲线下的面积分别为84.65%、86.06%、91.21%

半错误率分别为24.05%、21.05%、15.01%

均高于对比算法的结果。

Abstract

Aiming at the problem that the existing anti-spoofing detection algorithms difficultly detect unknown domain attacks

this paper proposes a face anti-spoofing detection algorithm using generative adversarial networks with hypercomplex wavelet transform to improve the ability of face recognition system to determine the face anti-spoofing. Four different types of datasets are adopted

in which three datasets are randomly selected as the training ones

and the remainder as the test dataset to serve for unknown face anti-spoofing detection during training. The training datasets are regarded as three source domain datasets

which are input into the generation network to make a feature generator against three discriminators. When this feature generator successfully deceives the three discriminators

a feature space sharing three source domains and being different from these domains is formed to detect the characteristics of the unknown domain data. The triple constraint functions inter-class and intra-class are set up to improve the performance of the discriminator

and the detailed subbands of the hypercomplex wavelet transform and the convolution network are combined to learn the detailed features in multiple directions of images. Then the depth map and remote photo plethysmography signal are embedded in the feature space to enhance the generalization performance of the generated feature space for the living face features. The test dataset for discriminative classification in the feature space is used to obtain live/fake results. The results show that on the CASIA-FASD

Replay-Attack and NUAA datasets

the proposed algorithm gets the AUC of 84.65%

86.06% and 91.21%

the HTER of 24.05%

21.05% and 15.01%

which are higher than those of the comparative algorithms.

关键词

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references

谢文化. 人像识别的技术演进路线 [J]. 中国公共安全, 2017(7): 135-138.

杨艺芳, 王宇平. 一种鉴别稀疏局部保持投影的人脸识别算法 [J]. 西安交通大学学报, 2016, 50(6): 54-60.

YANG Yifang, WANG Yuping. A face recognition algorithm based on discriminant sparse locality and preserving projections [J]. Journal of Xi'an Jiaotong University, 2016, 50(6): 54-60.

汪亚航, 宋晓宁, 吴小俊. 结合混合池化的双流人脸活体检测网络 [J]. 中国图象图形学报, 2020, 25(7): 1408-1420.

WANG Yahang, SONG Xiaoning, WU Xiaojun. Two-stream face spoofing detection network combined with hybrid pooling [J]. Journal of Image and Graphics, 2020, 25(7): 1408-1420.

BHARADWAJ S, DHAMECHA T I, VATSA M, et al. Computationally efficient face spoofing detection with motion magnification [C]∥Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway, NJ, USA: IEEE, 2013: 105-110.

SIDDIQUI T A, BHARADWAJ S, DHAMECHA T I, et al. Face anti-spoofing with multifeature videolet aggregation [C]∥Proceedings of the 23rd IEEE International Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2016: 1035-1040.

TIRUNAGARI S, POH N, WINDRIDGE D, et al. Detection of face spoofing using visual dynamics [J]. IEEE Transactions on Information Forensics and Security, 2015, 10(4): 762-777.

LI X B, KOMULAINEN J, ZHAO G Y, et al. Generalized face anti-spoofing by detecting pulse from face videos [C]∥Proceedings of the 23rd IEEE International Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2016: IEEE, 2016: 4244-4249.

XU Z Q, LI S, DENG W H. Learning temporal features using LSTM-CNN architecture for face anti-spoofing [C]∥Proceedings of the 3rd IEEE Asian Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2015: 141-145.

ATOUM Y, LIU Y J, JOURABLOO A, et al. Face anti-spoofing using patch and depth-based CNNs [C]∥Proceedings of the 2017 IEEE International Joint Conference on Biometrics. Piscataway, NJ, USA: IEEE, 2017: 319-328.

LIU Y J, JOURABLOO A, LIU X M. Learning deep models for face anti-spoofing: binary or auxiliary supervision [C]∥Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2018: 389-398.

SONG Xiao, ZHAO Xu, FANG Liangji, et al. Discriminative representation combinations for accurate face spoofing detection [J]. Pattern Recognition, 2019, 85: 220-231.

ZHNAG Peng, ZOU Fuhao, WU Zhiwen, et al. FeatherNets: convolutional neural networks as light as feather for face anti-spoofing [C]∥Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2019: 1574-1583.

YU Zitong, LI Xiaobai, NIU Xuesong, et al. Face anti-spoofing with human material perception [C]∥Proceedings of the 2020 European Conference on Computer Vision. Cham, Germany: Springer, 2020: 557-575.

YU Zitong, ZHAO Chenxu, WANG Zezheng, et al. Searching central difference convolutional networks for face anti-spoofing [C]∥Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2020: 5294-5304.

JOURABLOO A, LIU Yaojie, LIU Xiaoming. Face de-spoofing: anti-spoofing via noise modeling [C]∥Proceedings of the 2018 European Conference on Computer Vision. Cham, Germany: Springer, 2018: 297-315.

LUO S Y, KAN M, WU S Z, et al. Face anti-spoofing with multi-scale information [C]∥Proceedings of the 2018 IEEE Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 2018: 3402-3407.

SHAO R, LAN X Y, LI J W, et al. Multi-adversarial discriminative deep domain generalization for face presentation attack detection [C]∥Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2019: 10023-10031.

LIU Y J, STEHOUWER J, JOURABLOO A, et al. Deep tree learning for zero-shot face anti-spoofing [C]∥Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2019: 4680-4689.

BAYRO-CORROCHANO E. The theory and use of the quaternion wavelet transform [J]. Journal of Mathematical Imaging and Vision, 2006, 24(1): 19-35.

TAN Xiaoyang, LI Yi, LIU Jun, et al. Face liveness detection from a single image with sparse low rank bilinear discriminative model [C]∥Proceedings of the 2010 European Conference on Computer Vision. Cham, Germany: Springer, 2010: 504-517.

FENG Yao, WU Fan, SHAO Xiaohu, et al. Joint 3D face reconstruction and dense alignment with position map regression network [C]∥Proceedings of the 2018 European Conference on Computer Vision. Cham, Germany: Springer, 2018: 557-574.

DE HAAN G, JEANNE V. Robust pulse rate from chrominance-based rPPG [J]. IEEE Transactions on Bio-Medical Engineering, 2013, 60(10): 2878-2886.

ZHANG Z W, YAN J J, LIU S F, et al. A face anti-spoofing database with diverse attacks [C]∥Proceedings of the 2012 IEEE International Conference on Biometrics. Piscataway, NJ, USA: IEEE, 2012: 26-31.

CHINGOVSKA I, ANJOS A, MARCEL S. On the effectiveness of local binary patterns in face anti-spoofing [C]∥Proceedings of the IEEE International Conference of Biometrics Special Interest Group. Piscataway, NJ, USA: IEEE, 2012: 6313548.

WEN D, HAN H, JAIN A K. Face spoof detection with image distortion analysis [J]. IEEE Transactions on Information Forensics and Security, 2015, 10(4): 746-761.

MÄÄTTÄ J, HADID A, PIETIKÄINEN M. Face spoofing detection from single images using micro-texture analysis [C]∥Proceedings of the 2011 IEEE International Joint Conference on Biometrics. Piscataway, NJ, USA: IEEE, 2011: 6117510.

YANG J W, LEI Z, LI S Z. Learn convolutional neural network for face anti-spoofing [EB/OL]. [2020-08-01]. https:∥arxiv.org/abs/1408. 5601.

BOULKENAFET Z, KOMULAINEN J, HADID A. Face spoofing detection using colour texture analysis [J]. IEEE Transactions on Information Forensics and Security, 2016, 11(8): 1818-1830.

LI H L, PAN S J, WANG S Q, et al. Domain generalization with adversarial feature learning [C]∥Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2018: 5400-5409.

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