Abduh, LatifahIvrissimtzis, IoannisXu, Kai and Turner, Martin2021-09-072021-09-072021978-3-03868-158-8https://doi.org/10.2312/cgvc.20211312https://diglib.eg.org:443/handle/10.2312/cgvc20211312Anomaly detection, which is approaching the problem of face anti-spoofing as a one-class classification problem, is emerging as an increasingly popular alternative to the traditional approach of training binary classifiers on specialized anti-spoofing databases which contain both client and imposter samples. In this paper, we discuss the training protocols in the existing work on anomaly detection for face anti-spoofing, and note that they use images exclusively from specialized anti-spoofing databases, even though only common images of real faces are needed. In a proof-of-concept experiment, we demonstrate the potential benefits of adding in the anomaly detection training sets images from general face recognition, rather than specialised face anti-spoofing, databases, or images from the in-the-wild images. We train a convolutional autoencoder on real faces and compare the reconstruction error against a threshold to classify a face image 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 on an unseen database, as evidenced by an increase in the value of the Area Under the Curve.Computing methodologiesComputer vision tasksImage manipulation10.2312/cgvc.2021131221-26