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.
arXiv:2606. 31664v1 Announce Type: cross Abstract: Performance in face and speaker verification is largely driven by margin-penalty softmax losses such as CosFace and ArcFace.
By Dimitrios Koutsianos, Ladislav Mo\v{s}ner, Yannis Panagakis, Themos Stafylakis
arXiv:2606. 27855v1 Announce Type: cross Abstract: Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are available to train fully generic decoders.
By Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis
arXiv:2606. 11505v1 Announce Type: cross Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access.
By Kumar Kartikey, Nikos Komninos
arXiv:2607. 03783v1 Announce Type: new Abstract: Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition.
By Hamed Rafiei, Ali Mousavi
arXiv:2601. 04181v2 Announce Type: replace Abstract: Reliable long-term decoding of gestures from surface electromyography (EMG) is hindered by signal drift caused by electrode displacement, muscle fatigue, and/or posture changes.
By Nia Touko, Matthew O A Ellis, Cristiano Capone, Alessio Burrello, Elisa Donati, Luca Manneschi