Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D Cascaded AdaBoost for Eye Localization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Non-intrusive liveness detection by face images
Image and Vision Computing
Face liveness detection from a single image with sparse low rank bilinear discriminative model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Computer Vision Using Local Binary Patterns
Computer Vision Using Local Binary Patterns
Competition on counter measures to 2-D facial spoofing attacks
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Counter-measures to photo attacks in face recognition: A public database and a baseline
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Face spoofing detection from single images using micro-texture analysis
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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While there is a significant number of works addressing e.g. pose and illumination variation problems in face recognition, the vulnerabilities to spoofing attacks were mostly unexplored until very recently when an increasing attention is started to be paid to this threat. A spoofing attack occurs when a person tries to masquerade as someone else e.g. by wearing a mask to gain illegitimate access and advantages. This work provides the first investigation in research literature on the use of dynamic texture for face spoofing detection. Unlike masks and 3D head models, real faces are indeed non-rigid objects with contractions of facial muscles which result in temporally deformed facial features such as eye lids and lips. Our key idea is to learn the structure and the dynamics of the facial micro-textures that characterise only real faces but not fake ones. Hence, we introduce a novel and appealing approach to face spoofing detection using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern approach. We experiment with two publicly available databases consisting of several fake face attacks of different natures under varying conditions and imaging qualities. The experiments show excellent results beyond the state-of-the-art.