  The traditional approach to face anti-spoofing sees it as a binary
classification problem, and binary classifiers are trained and validated on
specialized anti-spoofing databases. One of the drawbacks of this approach is
that, due to the variability of face spoofing attacks, environmental factors,
and the typically small sample size, such classifiers do not generalize well to
previously unseen databases. Anomaly detection, which approaches face
anti-spoofing as a one-class classification problem, is emerging as an
increasingly popular alternative approach. Nevertheless, in all existing work
on anomaly detection for face anti-spoofing, the proposed training protocols
utilize images from specialized anti-spoofing databases only, even though only
common images of real faces are needed. Here, we explore the use of in-the-wild
images, and images from non-specialized face databases, to train one-class
classifiers for face anti-spoofing. Employing a well-established technique, we
train a convolutional autoencoder on real faces and compare the reconstruction
error of the input against a threshold to classify a face image accordingly as
either client or imposter.
  Our results show that the inclusion in the training set of in-the-wild images
increases the discriminating power of the classifier significantly on an unseen
database, as evidenced by a large increase in the value of the Area Under the
Curve. In a limitation of our approach, we note that the problem of finding a
suitable operating point on the unseen database remains a challenge, as
evidenced by the values of the Half Total Error Rate.
