Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Alonso Fernández, Fernando"'
We apply pre-trained architectures, originally developed for the ImageNet Large Scale Visual Recognition Challenge, for periocular recognition. These architectures have demonstrated significant success in various computer vision tasks beyond the ones
Externí odkaz:
http://arxiv.org/abs/2407.19472
Autor:
Alonso-Fernandez, Fernando, Hernandez-Diaz, Kevin, Rubio, Jose Maria Buades, Tiwari, Prayag, Bigun, Josef
The widespread use of mobile devices for all kind of transactions makes necessary reliable and real-time identity authentication, leading to the adoption of face recognition (FR) via the cameras embedded in such devices. Progress of deep Convolutiona
Externí odkaz:
http://arxiv.org/abs/2405.18302
Our study provides evidence that CNNs struggle to effectively extract orientation features. We show that the use of Complex Structure Tensor, which contains compact orientation features with certainties, as input to CNNs consistently improves identif
Externí odkaz:
http://arxiv.org/abs/2404.15608
Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes
Externí odkaz:
http://arxiv.org/abs/2404.05723
Robots are being designed to help people in an increasing variety of settings--but seemingly little attention has been given so far to the specific needs of women, who represent roughly half of the world's population but are highly underrepresented i
Externí odkaz:
http://arxiv.org/abs/2404.04123
We present a model-based feature extractor to describe neighborhoods around keypoints by finite expansion, estimating the spatially varying orientation by harmonic functions. The iso-curves of such functions are highly symmetric w.r.t. the origin (a
Externí odkaz:
http://arxiv.org/abs/2311.01651
In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution
Externí odkaz:
http://arxiv.org/abs/2311.01241
The proliferation of cameras and personal devices results in a wide variability of imaging conditions, producing large intra-class variations and a significant performance drop when images from heterogeneous environments are compared. However, many a
Externí odkaz:
http://arxiv.org/abs/2311.01237
In this paper, we present a new approach for facial anonymization in images and videos, abbreviated as FIVA. Our proposed method is able to maintain the same face anonymization consistently over frames with our suggested identity-tracking and guarant
Externí odkaz:
http://arxiv.org/abs/2309.04228
Autor:
Kolf, Jan Niklas, Boutros, Fadi, Elliesen, Jurek, Theuerkauf, Markus, Damer, Naser, Alansari, Mohamad, Hay, Oussama Abdul, Alansari, Sara, Javed, Sajid, Werghi, Naoufel, Grm, Klemen, Štruc, Vitomir, Alonso-Fernandez, Fernando, Diaz, Kevin Hernandez, Bigun, Josef, George, Anjith, Ecabert, Christophe, Shahreza, Hatef Otroshi, Kotwal, Ketan, Marcel, Sébastien, Medvedev, Iurii, Jin, Bo, Nunes, Diogo, Hassanpour, Ahmad, Khatiwada, Pankaj, Toor, Aafan Ahmad, Yang, Bian
This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further developme
Externí odkaz:
http://arxiv.org/abs/2308.04168