Training Dataset Construction for Anomaly Detection in Face Anti-spoofing

Abduh, Latifah; Ivrissimtzis, Ioannis

Training Dataset Construction for Anomaly Detection in Face Anti-spoofing

Files

021-026.pdf (482.25 KB)

Date

2021

Authors

Abduh, Latifah
Ivrissimtzis, Ioannis

Publisher

The Eurographics Association

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.

        @inproceedings{10.2312:cgvc.20211312
,
booktitle = {Computer Graphics and Visual Computing (CGVC)
},
editor = {Xu, Kai and Turner, Martin
},
title = {{Training Dataset Construction for Anomaly Detection in Face Anti-spoofing
}},
author = {Abduh, Latifah and 
Ivrissimtzis, Ioannis
},
year = {2021
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-158-8
},
DOI = {10.2312/cgvc.20211312
}
}