这篇论文直接优化EER指标，在18.5万人击键数据上EER降了30%，训练还更快，做生物识别的值得一看。
该论文提出EERLoss，一种可微且任意精度的等错误率（EER）近似损失函数，直接优化生物特征验证的主要评价指标。在KVC-onGoing基准上测试，涵盖超18.5万被试的击键动力学数据。消融实验表明EERLoss优于现有损失函数，且收敛更快。用EERLoss重新训练KVC-winning架构后，EER相对降低约30%，显著超越原SOTA。
EERLoss: A Novel Loss Function for Training Deep Biometric Models. A Case Study in Keystroke Dynamics
Deep learning approaches to biometric verification are commonly trained by optimizing indirect objectives, creating a misalignment between the optimization process and the primary evaluation metric, typically the Equal Error Rate (EER). This paper introduces EERLoss: a subdifferentiable, arbitrarily accurate approximation to EER for training deep biometric models. Furthermore, this framework has the potential to be adapted to optimize any specific operating point on the DET curve, enhancing its generalizability. To validate this approach, EERLoss is evaluated on a particularly demanding behavioral biometric modality: keystroke dynamics verification. This task is characterized by its high intra-class and low inter-class variability. Experiments are conducted on the large-scale KVC-onGoing benchmark, incorporating data from over 185,000 subjects across different scenarios. A comprehensive ablation study initially demonstrates the superiority of EERLoss in comparison to existing state-of-the-art loss functions. It also converges substantially faster compared to other losses, reducing the overall training cost. Additionally, a comparison is made between the proposed loss and the KVC-winning architecture by re-training it with EERLoss, demonstrating that the proposed approach significantly outperforms the original SoTA, achieving a relative EER reduction of up to approx. 30\%. This improvement on a challenging, large-scale benchmark validates the effectiveness of EERLoss as a task-aligned training objective specifically suited for high-variance biometric traits.