Face spoofing detection using multi-level local phase quantization (ML-LPQ)

Face spoofing detection using multi-level local phase quantization (ML-LPQ)

One complete video set for an individual subject of CASIA Face Anti-Spoofing Database.


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


Biometric technologies are becoming the foundation of an extensive array of highly secure identification and verification solution. Unfortunately, biometric systems are vulnerable to attacks made by persons showings photo, video or mask to spoof the real identity. In this paper we study a solution for those problems. We try to make solution to face spoofing for distinguishing between real face and fake one. Our approach called Multi-Level Local Phase Quantization (ML-LPQ) is focused in Local Phase Quantization (LPQ) descriptor for extracting features on face region of interest. In our approach, we use three levels for the LPQ descriptor to extract features and LibSVM for classification. Our experimental analysis on a publicly available CASIA face anti-spoofing database give us good result compared to other approaches using the same protocol.

Please cite as:

author={A. Benlamoudi and D. Samai and A. Ouafi and S. E. Bekhouche and A. Taleb-Ahmed and A. Hadid},
booktitle={International Conference on Automatic control, Telecommunications and Signals (ICATS15)},
title={Face spoofing detection using Multi-Level Local Phase Quantization (ML-LPQ)},


Paper: PDF
Database: CASIA