Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR).
Using printed photograph and replaying videos of biometric modalities, such as iris, fingerprint and face, are common attacks to fool the recognition systems for granting access as the genuine user.
In this work, motivated by the noise modeling and denoising algorithms, we identify a new problem of face de-spoofing, for the purpose of anti-spoofing: inversely decomposing a spoof face into a spoof noise and a live face, and then utilizing the spoof noise for classification.
We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data.
Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e. g, face recognition and 3D face reconstruction.
Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data.