An anomaly detection approach to face spoofing detection: A new formulation and evaluation protocol

Abstract

Face anti-spoofing problem can be quite challenging due to various factors including diversity of face spoofing attacks, any new means of spoofing, the problem of imaging sensor interoperability and other environmental factors in addition to the small sample size. Taking into account these observations, in this work, first, a new evaluation protocol called “innovative attack evaluation protocol” to study the effect of occurrence of unseen attack types is proposed which better reflects the realistic conditions in spoofing attacks. Second, a new formulation of the problem based on the anomaly detection concept is proposed where the training data comes from the positive class only. The test data, of course, may come from the positive or negative class. Finally, a thorough evaluation and comparison of 20 different one-class and two-class systems is performed and demonstrated that the anomaly-based formulation is not inferior as compared with the conventional two-class approach.