no code implementations • 2 Nov 2021 • Julian Fierrez, Javier Galbally, Javier Ortega-Garcia, Manuel R Freire, Fernando Alonso-Fernandez, Daniel Ramos, Doroteo Torre Toledano, Joaquin Gonzalez-Rodriguez, Juan A Siguenza, Javier Garrido-Salas, E Anguiano, Guillermo Gonzalez-de-Rivera, Ricardo Ribalda, Marcos Faundez-Zanuy, JA Ortega, Valentín Cardeñoso-Payo, A Viloria, Carlos E Vivaracho, Q Isaac Moro, Juan J Igarza, J Sanchez, Inmaculada Hernaez, Carlos Orrite-Urunuela, Francisco Martinez-Contreras, Juan José Gracia-Roche
A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol.
1 code implementation • 22 Mar 2020 • Juan Maroñas, Daniel Ramos, Roberto Paredes
Data Augmentation (DA) strategies have been proposed to regularize these models, being Mixup one of the most popular due to its ability to improve the accuracy, the uncertainty quantification and the calibration of DNN.
no code implementations • 18 Sep 2019 • Daniel Ramos, Juan Maroñas, Alicia Lozano-Diez
This paper explores several strategies for Forensic Voice Comparison (FVC), aimed at improving the performance of the LRs when using generative Gaussian score-to-LR models.
1 code implementation • 23 Aug 2019 • Juan Maroñas, Roberto Paredes, Daniel Ramos
Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks.
no code implementations • 21 Mar 2019 • Juan Maroñas, Roberto Paredes, Daniel Ramos
The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail.
no code implementations • 23 Oct 2018 • Ram P. Krish, Julian Fierrez, Daniel Ramos, Fernando Alonso-Fernandez, Josef Bigun
We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.
no code implementations • 27 Sep 2018 • Juan Maroñas, Roberto Paredes, Daniel Ramos
We apply Bayesian Neural Networks to improve calibration of state-of-the-art deep neural networks.