EERLoss: A Novel Loss Function for Training Deep Biometric Models. A Case Study in Keystroke Dynamics
arXiv:2606.24586v1 Announce Type: cross Abstract: 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, EERLo
The increasing sophistication and widespread adoption of deep learning in critical security applications necessitate more robust and accurate training methodologies.
Improving the accuracy and reliability of biometric models, by directly optimizing for EER, could significantly enhance security systems and reduce verification errors.
This novel loss function allows deep biometric models to be trained on a primary evaluation metric, potentially leading to more secure and generalizable AI systems.
- · Biometric security providers
- · AI model developers
- · Cybersecurity sector
- · Attackers breaching biometric systems
More secure and reliable biometric identification systems are developed and deployed.
Reduced incidence of identity theft and unauthorized access across various digital and physical domains.
Increased public and institutional trust in AI-powered security solutions, potentially accelerating their integration into sensitive applications.
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Read at arXiv cs.LG