RT Journal Article
T1 FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance System.
A1 Ruiz-Beltrán, Camilo Andrés
A1 Romero-Garces, Adrian
A1 González-García, Martín
A1 Marfil-Robles, Rebeca
A1 Bandera-Rubio, Antonio Jesús
K1 Iris (Anatomía) - Reconocimiento
K1 Redes neuronales (Informática)
AB Neural networks are the state-of-the-art solution to image-processing tasks. Some of these neural networks are relatively simple, but the popular convolutional neural networks (CNNs) can consist of hundreds of layers. Unfortunately, the excellent recognition accuracy of CNNs comes at the cost of very high computational complexity, and one of the current challenges is managing the power, delay and physical size limitations of hardware solutions dedicated to accelerating their inference process. In this paper, we describe the embedding of an eye detection system on a Zynq XCZU4EV UltraScale+ multiprocessor system-on-chip (MPSoC).
YR 2023
FD 2023-11-20
LK https://hdl.handle.net/10630/30091
UL https://hdl.handle.net/10630/30091
LA eng
NO Ruiz-Beltrán, C.A.; Romero-Garcés, A.; González-García, M.; Marfil, R.; Bandera, A. FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance System. Electronics 2023, 12, 4713.
NO This work has been supported by grants CPP2021-008931, PDC2022-133597-C42, TED2021-131739B-C21 and PID2022-137344OB-C32, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR (for the first three grants), and “ERDF A way of making Europe” (for the fourth grant).10% Partial funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026