Face spoofing detection using local binary patterns and Fisher Score

Face spoofing detection using local binary patterns and fisher score

The proposed approach


Azeddine Benlamoudi1, Djamel Samai1, Abdelkrim Ouafi2, Salah Eddine Bekhouche2, Abdelmalik Taleb-Ahmed3 and Abdenour Hadid4

  1. Laboratory of LAGE, University of Ouargla, Algeria
  2. Laboratory of LESIA, University of Biskra, Algeria
  3. Laboratory of LAMIH, University of Valenciennes, France
  4. Center for Machine Vision Research, University of Oulu, Finland


Todays biometric systems are vulnerable to spoof attacks made by non-real faces. The problem is when a person shows in front of camera a print photo or a picture from cell phone. We study in this paper an anti-spoofing solution for distinguishing between ‘live’ and ‘fake’ faces. In our approach we used overlapping block LBP operator to extract features in each region of the image. To reduce the features we used Fisher-Score. Finally, we used a nonlinear Support Vector Machine (SVM) classifier with kernel function for determining whether the input image corresponds to a live face or not. Our experimental analysis on a publicly available NUAA and CASIA face anti-spoofing databases following the standard protocols showed good results.

Please cite as:

author={A. Benlamoudi and D. Samai and A. Ouafi and S. E. Bekhouche and A. Taleb-Ahmed and A. Hadid},
booktitle={2015 3rd International Conference on Control, Engineering Information Technology (CEIT)},
title={Face spoofing detection using local binary patterns and Fisher Score},


Paper: PDF
Databases: CASIA and NUAA