Integration of image quality and motion cues for face anti-spoofing : A neural network approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

204 Scopus Citations
View graph of relations


  • Litong Feng
  • Yuming Li
  • Xuyuan Xu
  • Fang Yuan
  • Terence Chun-Ho Cheung
  • Kwok-Wai Cheung


Original languageEnglish
Pages (from-to)451-460
Journal / PublicationJournal of Visual Communication and Image Representation
Online published1 Apr 2016
Publication statusPublished - Jul 2016


Many trait-specific countermeasures to face spoofing attacks have been developed for security of face authentication. However, there is no superior face anti-spoofing technique to deal with every kind of spoofing attack in varying scenarios. In order to improve the generalization ability of face anti-spoofing approaches, an extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection. Shearlet is utilized to develop an image quality-based liveness feature. Dense optical flow is utilized to extract motion-based liveness features. A bottleneck feature fusion strategy can integrate different liveness features effectively. The proposed approach was evaluated on three public face anti-spoofing databases. A half total error rate (HTER) of 0% and an equal error rate (EER) of 0% were achieved on both REPLAY-ATTACK database and 3D-MAD database. An EER of 5.83% was achieved on CASIA-FASD database.

Research Area(s)

  • Dense optical flow, Face anti-spoofing, Feature fusion, Neural network, Shearlet

Citation Format(s)