Zobrazeno 1 - 10
of 251
pro vyhledávání: '"Damer, Naser"'
Autor:
Tapia, Juan E., Damer, Naser, Busch, Christoph, Espin, Juan M., Barrachina, Javier, Rocamora, Alvaro S., Ocvirk, Kristof, Alessio, Leon, Batagelj, Borut, Patwardhan, Sushrut, Ramachandra, Raghavendra, Mudgalgundurao, Raghavendra, Raja, Kiran, Schulz, Daniel, Aravena, Carlos
This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB2024). The competition attracted a total of ten registered teams, both from academia an
Externí odkaz:
http://arxiv.org/abs/2409.00372
Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detection plays a critical role in preven
Externí odkaz:
http://arxiv.org/abs/2408.12990
Autor:
Huber, Marco, Damer, Naser
The need for more transparent face recognition (FR), along with other visual-based decision-making systems has recently attracted more attention in research, society, and industry. The reasons why two face images are matched or not matched by a deep
Externí odkaz:
http://arxiv.org/abs/2407.11941
Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model. In this paper, we present an adaptive KD approach, namely AdaDistill, for deep face recognition
Externí odkaz:
http://arxiv.org/abs/2407.01332
Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-t
Externí odkaz:
http://arxiv.org/abs/2404.12203
Autor:
DeAndres-Tame, Ivan, Tolosana, Ruben, Melzi, Pietro, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, Morales, Aythami, Fierrez, Julian, Ortega-Garcia, Javier, Zhong, Zhizhou, Huang, Yuge, Mi, Yuxi, Ding, Shouhong, Zhou, Shuigeng, He, Shuai, Fu, Lingzhi, Cong, Heng, Zhang, Rongyu, Xiao, Zhihong, Smirnov, Evgeny, Pimenov, Anton, Grigorev, Aleksei, Timoshenko, Denis, Asfaw, Kaleb Mesfin, Low, Cheng Yaw, Liu, Hao, Wang, Chuyi, Zuo, Qing, He, Zhixiang, Shahreza, Hatef Otroshi, George, Anjith, Unnervik, Alexander, Rahimi, Parsa, Marcel, Sébastien, Neto, Pedro C., Huber, Marco, Kolf, Jan Niklas, Damer, Naser, Boutros, Fadi, Cardoso, Jaime S., Sequeira, Ana F., Atzori, Andrea, Fenu, Gianni, Marras, Mirko, Štruc, Vitomir, Yu, Jiang, Li, Zhangjie, Li, Jichun, Zhao, Weisong, Lei, Zhen, Zhu, Xiangyu, Zhang, Xiao-Yu, Biesseck, Bernardo, Vidal, Pedro, Coelho, Luiz, Granada, Roger, Menotti, David
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRw 2024)
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some c
Externí odkaz:
http://arxiv.org/abs/2404.10378
Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQ
Externí odkaz:
http://arxiv.org/abs/2404.09555
Autor:
Shahreza, Hatef Otroshi, Ecabert, Christophe, George, Anjith, Unnervik, Alexander, Marcel, Sébastien, Di Domenico, Nicolò, Borghi, Guido, Maltoni, Davide, Boutros, Fadi, Vogel, Julia, Damer, Naser, Sánchez-Pérez, Ángela, EnriqueMas-Candela, Calvo-Zaragoza, Jorge, Biesseck, Bernardo, Vidal, Pedro, Granada, Roger, Menotti, David, DeAndres-Tame, Ivan, La Cava, Simone Maurizio, Concas, Sara, Melzi, Pietro, Tolosana, Ruben, Vera-Rodriguez, Ruben, Perelli, Gianpaolo, Orrù, Giulia, Marcialis, Gian Luca, Fierrez, Julian
Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating syn
Externí odkaz:
http://arxiv.org/abs/2404.04580
Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concer
Externí odkaz:
http://arxiv.org/abs/2404.03537
Autor:
Stragapede, Giuseppe, Vera-Rodriguez, Ruben, Tolosana, Ruben, Morales, Aythami, DeAndres-Tame, Ivan, Damer, Naser, Fierrez, Julian, Garcia, Javier-Ortega, Gonzalez, Nahuel, Shadrikov, Andrei, Gordin, Dmitrii, Schmitt, Leon, Wimmer, Daniel, Grossmann, Christoph, Krieger, Joerdis, Heinz, Florian, Krestel, Ron, Mayer, Christoffer, Haberl, Simon, Gschrey, Helena, Yamagishi, Yosuke, Saha, Sanjay, Rasnayaka, Sanka, Wickramanayake, Sandareka, Sim, Terence, Gutfeter, Weronika, Baran, Adam, Krzyszton, Mateusz, Jaskola, Przemyslaw
This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over
Externí odkaz:
http://arxiv.org/abs/2401.16559