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Your search keyword '"Morales, Aythami"' showing total 93 results
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93 results on '"Morales, Aythami"'

1. FRCSyn Challenge at WACV 2024: Face Recognition Challenge in the Era of Synthetic Data

Author

Melzi, Pietro, primary, Tolosana, Ruben, additional, Vera-Rodriguez, Ruben, additional, Kim, Minchul, additional, Rathgeb, Christian, additional, Liu, Xiaoming, additional, DeAndres-Tame, Ivan, additional, Morales, Aythami, additional, Fierrez, Julian, additional, Ortega-Garcia, Javier, additional, Zhao, Weisong, additional, Zhu, Xiangyu, additional, Yan, Zheyu, additional, Zhang, Xiao-Yu, additional, Wu, Jinlin, additional, Lei, Zhen, additional, Tripathi, Suvidha, additional, Kothari, Mahak, additional, Zama, Md Haider, additional, Deb, Debayan, additional, Biesseck, Bernardo, additional, Vidal, Pedro, additional, Granada, Roger, additional, Fickel, Guilherme, additional, Führ, Gustavo, additional, Menotti, David, additional, Unnervik, Alexander, additional, George, Anjith, additional, Ecabert, Christophe, additional, Shahreza, Hatef Otroshi, additional, Rahimi, Parsa, additional, Marcel, Sébastien, additional, Sarridis, Ioannis, additional, Koutlis, Christos, additional, Baltsou, Georgia, additional, Papadopoulos, Symeon, additional, Diou, Christos, additional, Di Domenico, Nicolò, additional, Borghi, Guido, additional, Pellegrini, Lorenzo, additional, Mas-Candela, Enrique, additional, Sánchez-Pérez, Ángela, additional, Atzori, Andrea, additional, Fenu, Gianni, additional, Boutros, Fadi, additional, Marras, Mirko, additional, and Damer, Naser, additional

Published
2024
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2. IEEE BigData 2023 Keystroke Verification Challenge (KVC)

Author

Stragapede, Giuseppe, primary, Vera-Rodriguez, Ruben, additional, Tolosana, Ruben, additional, Morales, Aythami, additional, DeAndres-Tame, Ivan, additional, Damer, Naser, additional, Fierrez, Julian, additional, Garcia, Javier-Ortega, additional, Gonzalez, Nahuel, additional, Shadrikov, Andrei, additional, Gordin, Dmitrii, additional, Schmitt, Leon, additional, Wimmer, Daniel, additional, Großmann, Christoph, additional, Krieger, Joerdis, additional, Heinz, Florian, additional, Krestel, Ron, additional, Mayer, Christoffer, additional, Haberl, Simon, additional, Gschrey, Helena, additional, Yamagishi, Yosuke, additional, Saha, Sanjay, additional, Rasnayaka, Sanka, additional, Wickramanayake, Sandareka, additional, Sim, Terence, additional, Gutfeter, Weronika, additional, Baran, Adam, additional, Krzysztón, Mateusz, additional, and Jaskóła, Przemysław, additional

Published
2023
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10. Kinematic Synthesis for 3D Signatures

Author

Diaz, Moises, primary, Ferrer, Miguel A., additional, Carmona-Duarte, Cristina, additional, Quintana, Jose Juan, additional, Morales, Aythami, additional, Fierrez, Julian, additional, and Plamondon, Rejean, additional

Published
2022
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11. IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)

Author

Stragapede, Giuseppe, primary, Vera-Rodriguez, Ruben, additional, Tolosana, Ruben, additional, Morales, Aythami, additional, Fierrez, Julian, additional, Ortega-Garcia, Javier, additional, Rasnayaka, Sanka, additional, Seneviratne, Sachith, additional, Dissanayake, Vipula, additional, Liebers, Jonathan, additional, Islam, Ashhadul, additional, Belhaouari, Samir Brahim, additional, Ahmad, Sumaiya, additional, and Jabin, Suraiya, additional

Published
2022
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13. FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliance

Author

Hernandez-Ortega, Javier, Fierrez, Julian, Gomez, Luis F., Morales, Aythami, Gonzalez-de-Suso, Jose Luis, and Zamora-Martinez, Francisco

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

In this paper we develop FaceQvec, a software component for estimating the conformity of facial images with each of the points contemplated in the ISO/IEC 19794-5, a quality standard that defines general quality guidelines for face images that would make them acceptable or unacceptable for use in official documents such as passports or ID cards. This type of tool for quality assessment can help to improve the accuracy of face recognition, as well as to identify which factors are affecting the quality of a given face image and to take actions to eliminate or reduce those factors, e.g., with postprocessing techniques or re-acquisition of the image. FaceQvec consists of the automation of 25 individual tests related to different points contemplated in the aforementioned standard, as well as other characteristics of the images that have been considered to be related to facial quality. We first include the results of the quality tests evaluated on a development dataset captured under realistic conditions. We used those results to adjust the decision threshold of each test. Then we checked again their accuracy on a evaluation database that contains new face images not seen during development. The evaluation results demonstrate the accuracy of the individual tests for checking compliance with ISO/IEC 19794-5. FaceQvec is available online (https://github.com/uam-biometrics/FaceQvec).

Published
2022
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30. SensitiveNets: Learning Agnostic Representations with Application to Face Images.

Author

Morales, Aythami, Fierrez, Julian, Vera-Rodriguez, Ruben, and Tolosana, Ruben

Subjects
*DATA protection, *DEEP learning
Abstract

This work proposes a novel privacy-preserving neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data. The new international regulation for personal data protection forces data controllers to guarantee privacy and avoid discriminative hazards while managing sensitive data of users. In our approach, privacy and discrimination are related to each other. Instead of existing approaches aimed directly at fairness improvement, the proposed feature representation enforces the privacy of selected attributes. This way fairness is not the objective, but the result of a privacy-preserving learning method. This approach guarantees that sensitive information cannot be exploited by any agent who process the output of the model, ensuring both privacy and equality of opportunity. Our method is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective. The method is evaluated on three different primary tasks (identity, attractiveness, and smiling) and three publicly available benchmarks. In addition, we present a new face annotation dataset with balanced distribution between genders and ethnic origins. The experiments demonstrate that it is possible to improve the privacy and equality of opportunity while retaining competitive performance independently of the task. [ABSTRACT FROM AUTHOR]

Published
2021
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38. KBOC: Keystroke biometrics OnGoing competition

Author

Morales, Aythami, primary, Fierrez, Julian, additional, Gomez-Barrero, Marta, additional, Ortega-Garcia, Javier, additional, Daza, Roberto, additional, Monaco, John V., additional, Montalvao, Jugurta, additional, Canuto, Janio, additional, and George, Anjith, additional

Published
2016
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44. One-handed Keystroke Biometric Identification Competition

Author

Monaco, John V., primary, Perez, Gonzalo, additional, Tappert, Charles C., additional, Bours, Patrick, additional, Mondal, Soumik, additional, Rajkumar, Sudalai, additional, Morales, Aythami, additional, Fierrez, Julian, additional, and Ortega-Garcia, Javier, additional

Published
2015
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45. A Behavioral Handwriting Model for Static and Dynamic Signature Synthesis.

Author

Ferrer, Miguel A., Diaz, Moises, Carmona-Duarte, Cristina, and Morales, Aythami

Subjects
WRITING, EDUCATION, DATABASE management, INTEGRATED software, PROBABILITY theory
Abstract

The synthetic generation of static handwritten signatures based on motor equivalence theory has been recently proposed for biometric applications. Motor equivalence divides the human handwriting action into an effector dependent cognitive level and an effector independent motor level. The first level has been suggested by others as an engram, generated through a spatial grid, and the second has been emulated with kinematic filters. Our paper proposes a development of this methodology in which we generate dynamic information and provide a unified comprehensive synthesizer for both static and dynamic signature synthesis. The dynamics are calculated by lognormal sampling of the 8-connected continuous signature trajectory, which includes, as a novelty, the pen-ups. The forgery generation imitates a signature by extracting the most perceptually relevant points of the given genuine signature and interpolating them. The capacity to synthesize both static and dynamic signatures using a unique model is evaluated according to its ability to adapt to the static and dynamic signature inter- and intra-personal variability. Our highly promising results suggest the possibility of using the synthesizer in different areas beyond the generation of unlimited databases for biometric training. [ABSTRACT FROM AUTHOR]

Published
2017
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