An Anomaly Detection Approach to Face Spoofing Detection: A New Formulation and Evaluation Protocol

Abstract

Face spoofing detection is commonly formulated as a two-class recognition problem where relevant features of both positive (real access) and negative samples (spoofing attempts) are utilized to train the system. However, the diversity of spoofing attacks, any new means of spoofing attackers, may invent (previously unseen by the system) the problem of imaging sensor interoperability, and other environmental factors in addition to the small sample size make the problem quite challenging. Considering these observations, in this paper, a number of propositions in the evaluation scenario, problem formulation, and solving are presented. First of all, a new evaluation protocol to study the effect of occurrence of unseen attack types, where the train and test data are produced by different means, is proposed. The new evaluation protocol better reflects the realistic conditions in spoofing attempts where an attacker may come up with new means for spoofing. Inter-database and intra-database experiments are incorporated into the evaluation scheme to account for the sensor interoperability problem. Second, a new and more realistic formulation of the spoofing detection problem based on the anomaly detection concept is proposed where the training data come from the positive class only. The test data, of course, may come from the positive or negative class. Such a one-class formulation circumvents the need for the availability of negative training samples, which, in an in deal case, should be the representative of all possible spoofing types. Finally, a thorough evaluation and comparison of 20 different one-class and two-class systems on the video sequences of three widely employed databases is performed to investigate the merits of the one-class anomaly detection approaches compared with the common two-class formulations. It is demonstrated that the anomaly-based formulation is not inferior as compared with the conventional two-class approach.