基于面部生命特征的3D假面欺骗攻击检测方法

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (2): 361-368.

基于面部生命特征的3D假面欺骗攻击检测方法

谷小婧, 付传卿, 顾幸生

华东理工大学化工过程先进控制与优化技术教育部重点实验室,上海 200237

收稿日期:2015-04-13 修回日期:2015-09-11 出版日期:2016-02-08 发布日期:2020-08-17
作者简介:谷小婧(1983-),女,山东,博士,讲师,研究方向为机器视觉,模式识别,红外图像处理。
基金资助:
国家自然科学基金项目(61205017,61502293,61573144); 中央高校基本科研业务费专项资金项目

Facial Vital Sign Based Countermeasure Against 3D Mask Attacks

Gu Xiaojing, Fu Chuanqing, Gu Xingsheng

Key Laboratory of Advanced Control and Optimization for Chemical Process (Ministry of Education),East China University of Science and Technology, Shanghai 200237, China

Received:2015-04-13 Revised:2015-09-11 Online:2016-02-08 Published:2020-08-17

摘要/Abstract

摘要： 现有的人脸认证系统大都易于遭受欺骗攻击,传统的攻击方式主要包含影印照片和视频回放。随着3D打印技术的不断发展和成熟,使用3D面具进行欺骗攻击逐渐成为新的威胁。针对3D面具欺骗攻击提出了一种新的特征用于攻击检测。该特征基于人体心脏搏动会导致真实用户的面部血流量发生周期性变化这一生理现象,提取了面部皮肤颜色信号的频谱。在公开的3D面具欺骗攻击数据库上的试验表明,联合使用该生理特征和面部纹理特征的抗攻击方法相比于以往单独使用纹理特征的方法准确率得到了显著提升,可以更好地抵抗3D面具的欺骗攻击。

关键词: 人脸认证, 欺骗攻击, 3D打印面具, 面部生命特征

Abstract: Vulnerability to spoofing attacks is the main drawback for current face authentication systems. Traditional spoofing attacks include displaying printed photos and replaying recorded videos. With the development of 3D printing technology, the 3D mask spoofing attack has been becoming the new threat. A novel anti-spoofing feature was proposed to 3D mask attacks. A liveness feature from the power spectrum of facial color signal was extracted based on a physiological phenomenon that the color of real human face changed periodically due to the blood circulation. The performance of countermeasure jointly using the liveness feature and facial texture feature was evaluated on a public database named 3D Mask Attack Database (3DMAD) and achieved a higher accuracy comparing to previous methods that only considered texture features.

Key words: face authentication, spoofing attacks, 3D mask, facial vital signs

中图分类号:

TP391.4

谷小婧, 付传卿, 顾幸生. 基于面部生命特征的3D假面欺骗攻击检测方法[J]. 系统仿真学报, 2016, 28(2): 361-368.

Gu Xiaojing, Fu Chuanqing, Gu Xingsheng. Facial Vital Sign Based Countermeasure Against 3D Mask Attacks[J]. Journal of System Simulation, 2016, 28(2): 361-368.

参考文献 16

[1]	I Chingovska, A Anjos, S Marcel.On the effectiveness of local binary patterns in face anti-spoofing[C]// IEEE Biometrics Special Interest Group (BIOSIG), 2012. USA: IEEE, 2012.
[2]	J Maatta, A Hadid, M Pietika.Face spoofing detection from single images using texture and local shape analysis[J]. IET Biometrics (S2047-4938), 2012, 1(1): 3-10.
[3]	M Marsico, M Nappi, M Riccio, et al.Moving face spoofing detection via 3d projective invariants[C]// 5th IAPR International Conference on Biometrics (ICB), 2012. USA: IEEE, 2012.
[4]	G Pan, Z Wu, L Sun.Liveness detection for face recognition [Z]// Recent Advances in Face Recognition. Croatia: INTECH Open Access Publisher, 2008: 109-124.
[5]	S Bharadwaj, T I Dhamecha, M Vatsa, et al.Computationally Efficient Face Spoofing Detection with Motion Magnification[C]// IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). USA: IEEE, 2013: 105-110.
[6]	N Erdogmus, S Marcel.Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect[C]// Proceedings of IEEE International Conference on Biometrics: Theory, Applications and Systems, 2013. USA: IEEE, 2013.
[7]	Z Zhang, D Yi, Z Lei, et al.Face liveness detection by learning multi-spectral reflectance distributions[C]// IEEE International Conference on Automatic Face Gesture Recognition and Workshops, 2011. USA: IEEE, 2011: 436-441.
[8]	N Kose, J L Dugelay.Countermeasure for the protection of face recognition systems against mask attacks[C]// IEEE Automatic Face and Gesture Recognition, 2013. USA: IEEE, 2013.
[9]	N Kose, J L Dugelay.Shape and Texture Based Countermeasure to Protect Face Recognition Systems Against Mask Attacks[C]// IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2013. USA: IEEE, 2013: 111-116.
[10]	M Z Poh, D J McDuff, R W Picard. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation[J]. Optics Express (S1094-4087), 2010, 18(10): 10762-10774.
[11]	G Gibert, D D'Alessandro. Face detection method based on photoplethysmography[C]// IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2013. USA: IEEE, 2013: 449-453.
[12]	W Verkruysse, L O Svaasand, J S Nelson.Remote plethysmographic imaging using ambient light[J]. Optics Express (S1094-4087), 2008, 16(26): 21434-21445.
[13]	R Wallace, M McLaren, C McCool, et al.Inter-session variability modelling and joint factor analysis for face authentication[C]// IEEE International Joint Conference on Biometrics, 2011. USA: IEEE, 2011.
[14]	T Ojala, M Pietikainen, T Maenpaa.Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2002, 24(7): 971-987.
[15]	J Trefny, J Matas.Extended set of local binary patterns for rapid object detection[C]// Proceedings of the Computer Vision Winter Workshop, 2010. Czech Republic, 2010.
[16]	J Maatta, A Hadid, M Pietikainen.Face spoofing detection from single images using micro-texture analysis[C]// IEEE International Joint Conference on Biometrics, 2011. USA: IEEE, 2011.