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Integration of image quality and motion cues for face anti-spoofing: A neural network approach.
- Source :
-
Journal of Visual Communication & Image Representation . Jul2016, Vol. 38, p451-460. 10p. - Publication Year :
- 2016
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 38
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 115413716
- Full Text :
- https://doi.org/10.1016/j.jvcir.2016.03.019