t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators
September 21, 2023 ยท Entered Twilight ยท ๐ IEEE Transactions on Pattern Analysis and Machine Intelligence
Repo contents: .gitignore, LICENSE, README.md, eval_metrics.py, evaluate_tEER.py, loading_function.py, scores, simulated_function.py
Authors
Tomi Kinnunen, Kong Aik Lee, Hemlata Tak, Nicholas Evans, Andreas Nautsch
arXiv ID
2309.12237
Category
cs.CR: Cryptography & Security
Cross-listed
cs.LG,
cs.SD,
eess.AS,
eess.IV,
stat.CO
Citations
16
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Repository
https://github.com/TakHemlata/T-EER
โญ 14
Last Checked
1 month ago
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.
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