A database for face presentation attack using wax figure faces

Compared to 2D face presentation attacks (e.g. printed photos and video replays), 3D type attacks are more challenging to face recognition systems (FRS) by presenting 3D characteristics or materials similar to real faces. Existing 3D face spoofing databases, however, mostly based on 3D masks, are restricted to small data size or poor authenticity due to the production difficulty and high cost. In this work, we introduce the first wax figure face database, WFFD, as one type of super-realistic 3D presentation attacks to spoof the FRS. This database consists of 2200 images with both real and wax figure faces (totally 4400 faces) with a high diversity from online collections. Experiments on this database first investigate the vulnerability of three popular FRS to this kind of new attack. Further, we evaluate the performance of several face presentation attack detection methods to show the attack abilities of this super-realistic face spoofing database.

[1]  Stan Z. Li,et al.  Face liveness detection by learning multispectral reflectance distributions , 2011, Face and Gesture 2011.

[2]  Richa Singh,et al.  Face Presentation Attack with Latex Masks in Multispectral Videos , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[3]  Jukka Komulainen,et al.  Face anti-spoofing based on color texture analysis , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[4]  Sébastien Marcel,et al.  What You Can't See Can Help You - Extended-Range Imaging for 3D-Mask Presentation Attack Detection , 2017, 2017 International Conference of the Biometrics Special Interest Group (BIOSIG).

[5]  Jean-Luc Dugelay,et al.  Shape and Texture Based Countermeasure to Protect Face Recognition Systems against Mask Attacks , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[6]  Ajay Kumar,et al.  Detecting Presentation Attacks from 3D Face Masks Under Multispectral Imaging , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  Sébastien Marcel,et al.  Spoofing Face Recognition With 3D Masks , 2014, IEEE Transactions on Information Forensics and Security.

[8]  Junjie Yan,et al.  A face antispoofing database with diverse attacks , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[9]  Guoying Zhao,et al.  3D Mask Face Anti-spoofing with Remote Photoplethysmography , 2016, ECCV.

[10]  Pong C. Yuen,et al.  Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection , 2018, ECCV.

[11]  Farzin Deravi,et al.  Biometric Counter-Spoofing for Mobile Devices Using Gaze Information , 2017, PReMI.

[12]  Javier Hernandez-Ortega,et al.  Time Analysis of Pulse-Based Face Anti-Spoofing in Visible and NIR , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[13]  Jean-Luc Dugelay,et al.  On the vulnerability of face recognition systems to spoofing mask attacks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Liming Chen,et al.  3D Facial Geometric Attributes Based Anti-Spoofing Approach against Mask Attacks , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[15]  David Menotti,et al.  Deep Representations for Iris, Face, and Fingerprint Spoofing Detection , 2014, IEEE Transactions on Information Forensics and Security.

[16]  Mahadev Satyanarayanan,et al.  OpenFace: A general-purpose face recognition library with mobile applications , 2016 .

[17]  Pong C. Yuen,et al.  Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[18]  Kiran B. Raja,et al.  On the vulnerability of face recognition systems towards morphed face attacks , 2017, 2017 5th International Workshop on Biometrics and Forensics (IWBF).

[19]  Seongbeak Yoon,et al.  Masked fake face detection using radiance measurements. , 2009, Journal of the Optical Society of America. A, Optics, image science, and vision.

[20]  Xiaoli Hao,et al.  A New Multispectral Method for Face Liveness Detection , 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

[21]  Abdelmalik Taleb-Ahmed,et al.  Face spoofing detection using multi-level local phase quantization (ML-LPQ) , 2015 .

[22]  Yan Wang,et al.  Face Anti-spoofing to 3D Masks by Combining Texture and Geometry Features , 2018, CCBR.

[23]  Eduardo Valle,et al.  Transfer Learning Using Convolutional Neural Networks for Face Anti-spoofing , 2017, ICIAR.

[24]  Jean-Luc Dugelay,et al.  Reflectance analysis based countermeasure technique to detect face mask attacks , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[25]  Sébastien Marcel,et al.  Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[26]  A. Lakshmi,et al.  DEEP REPRESENTATIONS FOR IRIS , FACE , AND FINGERPRINT SPOOFING DETECTION , 2017 .

[27]  Matti Pietikäinen,et al.  Generalized face anti-spoofing by detecting pulse from face videos , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[28]  Farzin Deravi,et al.  Gaze stability for liveness detection , 2018, Pattern Analysis and Applications.

[29]  Javier Galbally,et al.  Three-dimensional and two-and-a-half-dimensional face recognition spoofing using three-dimensional printed models , 2016, IET Biom..

[30]  Yuchun Fang,et al.  Ultra-deep Neural Network for Face Anti-spoofing , 2017, ICONIP.

[31]  S Naveen,et al.  Face recognition and authentication using LBP and BSIF mask detection and elimination , 2016, 2016 International Conference on Communication Systems and Networks (ComNet).

[32]  Andreas Kolb,et al.  Reliable face anti-spoofing using multispectral SWIR imaging , 2016, 2016 International Conference on Biometrics (ICB).

[33]  Jean-Luc Dugelay,et al.  Countermeasure for the protection of face recognition systems against mask attacks , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[34]  Ramachandra Raghavendra,et al.  Novel presentation attack detection algorithm for face recognition system: Application to 3D face mask attack , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[35]  Farzin Deravi,et al.  Biometrie presentation attack detection using gaze alignment , 2018, 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA).

[36]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[37]  Guoying Zhao,et al.  A 3D Mask Face Anti-Spoofing Database with Real World Variations , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[38]  Keche Mokhtar,et al.  The detection of spoofing by 3D mask in a 2D identity recognition system , 2017, Egyptian Informatics Journal.

[39]  Richa Singh,et al.  Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning , 2017, IEEE Transactions on Information Forensics and Security.

[40]  Singh Richa,et al.  Face anti-spoofing using Haralick features , 2016 .