Face Anti-spoofing Based on General Image Quality Assessment

A new face anti-spoofing method based on general image quality assessment is presented. The proposed approach presents a very low degree of complexity which makes it suitable for real-time applications, using 14 image quality features extracted from one image (i.e., the same acquired for face recognition purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on two publicly available datasets, show very competitive results compared to other state-of-the-art methods tested on the same benchmarks. The findings presented in the work clearly suggest that the analysis of the general image quality of real face samples reveals highly valuable information that may be very efficiently used to discriminate them from fake images.

[1]  Julian Fiérrez,et al.  Author's Personal Copy Future Generation Computer Systems a High Performance Fingerprint Liveness Detection Method Based on Quality Related Features , 2022 .

[2]  Nasir D. Memon,et al.  Image manipulation detection , 2006, J. Electronic Imaging.

[3]  Nasir D. Memon,et al.  Steganalysis using image quality metrics , 2003, IEEE Trans. Image Process..

[4]  I. Pavlidis,et al.  The imaging issue in an automatic face/disguise detection system , 2000, Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (Cat. No.PR00640).

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

[6]  Sébastien Marcel,et al.  Can face anti-spoofing countermeasures work in a real world scenario? , 2013, 2013 International Conference on Biometrics (ICB).

[7]  Luminita Vasiu,et al.  Biometric Recognition - Security and Privacy Concerns , 2004, ICETE.

[8]  Tieniu Tan,et al.  Live face detection based on the analysis of Fourier spectra , 2004, SPIE Defense + Commercial Sensing.

[9]  Weisi Lin,et al.  Contrast signal-to-noise ratio for image quality assessment , 2005, IEEE International Conference on Image Processing 2005.

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

[11]  Matti Pietikäinen,et al.  Face spoofing detection from single images using micro-texture analysis , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[12]  Sébastien Marcel,et al.  On the effectiveness of local binary patterns in face anti-spoofing , 2012, 2012 BIOSIG - Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG).

[13]  Josef Bigün,et al.  Non-intrusive liveness detection by face images , 2009, Image Vis. Comput..

[14]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[15]  Josef Bigün,et al.  Audio-visual person authentication using lip-motion from orientation maps , 2007, Pattern Recognit. Lett..

[16]  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).

[17]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[18]  Sébastien Marcel,et al.  Counter-measures to photo attacks in face recognition: A public database and a baseline , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[19]  Sébastien Marcel,et al.  Bob: a free signal processing and machine learning toolbox for researchers , 2012, ACM Multimedia.

[20]  Chaminda T. E. R. Hewage,et al.  Image quality assessment based on edge preservation , 2012, Signal Process. Image Commun..

[21]  Bülent Sankur,et al.  Statistical evaluation of image quality measures , 2002, J. Electronic Imaging.

[22]  Julian Fiérrez,et al.  Iris liveness detection based on quality related features , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).