  Face anti-spoofing is the key to preventing security breaches in biometric
recognition applications. Existing software-based and hardware-based face
liveness detection methods are effective in constrained environments or
designated datasets only. Deep learning method using RGB and infrared images
demands a large amount of training data for new attacks. In this paper, we
present a face anti-spoofing method in a real-world scenario by automatic
learning the physical characteristics in polarization images of a real face
compared to a deceptive attack. A computational framework is developed to
extract and classify the unique face features using convolutional neural
networks and SVM together. Our real-time polarized face anti-spoofing (PAAS)
detection method uses a on-chip integrated polarization imaging sensor with
optimized processing algorithms. Extensive experiments demonstrate the
advantages of the PAAS technique to counter diverse face spoofing attacks
(print, replay, mask) in uncontrolled indoor and outdoor conditions by learning
polarized face images of 33 people. A four-directional polarized face image
dataset is released to inspire future applications within biometric
anti-spoofing field.
