Face Anti-Spoofing Combining MLLBP and MLBSIF

Face Anti-Spoofing Combining MLLBP and MLBSIF

Proposed approach


Azeddine Benlamoudi1, Fares Bougourzi2, Mohammed En-nadhir Zighem3, Salah Eddine Bekhouche3, Abdelkrim Ouafi3 and Abdelmalik Taleb-Ahmed4,

  1. Laboratory of LAGE, University of Ouargla, Algeria
  2. Laboratory of LTII, University of Béjaïa, Algeria
  3. Laboratory of LESIA, University of Biskra, Algeria
  4. Laboratory of LAMIH, University of Valenciennes, France


The Face recognition applications are the used way of authentication identity verification of mobile payment.
This popularity of face recognition is easy to raise concerns about face spoof attacks; use photo or video of an authorized person’s face to access to facilities or services. We propose an efficient and more robust algorithm for face spoof detection based-on combination between MLLBP and MLBSIF. An ensemble classifier, consisting of Lib-SVM classifiers using different face spoof attacks (e.g., printed photo and replayed video) to trained our model which is used to distinguish between genuine and spoof faces. We tested our approach on CASIA FASD database. Our proposed approach conduct a good result compared with the state of art.

Please cite as:

Title = {Face Anti-Spoofing Combining MLLBP and MLBSIF},
Author = {Benlamoudi, A. and Bougourzi, F. and Zighem, ME. and Bekhouche, SE. and Ouafi, A. and Taleb-Ahmed, A.},
Booktitle = {10ème Conférence sur le Génie Electrique},
Year = {2017},


Paper: PDF
Database: CASIA