Non‐reference image quality assessment and natural scene statistics to counter biometric sensor spoofing
Dominik Söllinger, Pauline Trung, Andreas Uhl · IET Biometrics · 2018
Non‐reference image quality measures (IQM) as well as their associated natural scene statistics (NSS) are used to distinguish real biometric data from fake data as used in presentation/sensor spoofing attacks. An experimental study shows that a support vector machine directly trained on NSS as used in blind/referenceless image spatial quality evaluator provides highly accurate classification of real versus fake iris, fingerprint, face, and fingervein data in generic manner. This contrasts to using the IQM directly, the accuracy of which turns out to be rather data set and parameter choice‐dependent. While providing very low average classification error rate values for complete training data, generalisation to unseen attack types is difficult in open‐set scenarios and obtained accuracy varies in almost unpredictable manner. This implies that for each given sensor/attack set‐up, the ability of the introduced methods to detect unseen attacks needs to be assessed separately.