Training Dataset Construction for Anomaly Detection in Face Anti-spoofing

Abstract

Anomaly 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.