t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators

TL;DR

The paper introduces t-EER, a parameter-free metric for jointly evaluating presentation attack detection and biometric verification systems, providing a stable, prevalence-independent measure applicable across modalities.

Contribution

It proposes the t-EER metric for tandem evaluation, addressing limitations of previous methods by being parameter-free and prevalence-invariant.

Findings

t-EER effectively evaluates PAD and biometric systems jointly.

The concurrent t-EER is stable across attack prevalence levels.

Demonstrated on simulated and real voice biometrics data.

Abstract

Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-systems operate in tandem to solve the single task of reliable biometric verification, they address different detection tasks and are hence typically evaluated separately. Evidence shows that this approach is suboptimal. We introduce a new metric for the joint evaluation of PAD solutions operating in situ with biometric verification. In contrast to the tandem detection cost function proposed recently, the new tandem equal error rate (t-EER) is parameter free. The combination of two classifiers nonetheless leads to a \emph{set} of operating points at which false alarm and miss rates are equal and also dependent upon the prevalence of attacks. We therefore introduce the \emph{concurrent} t-EER, a unique operating point which is invariable to the prevalence of attacks. Using both modality (and even application) agnostic simulated scores, as well as real scores for a voice biometrics application, we demonstrate application of the t-EER to a wide range of biometric system evaluations under attack. The proposed approach is a strong candidate metric for the tandem evaluation of PAD systems and biometric comparators.