2026-06-05T19:16:24Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/300912026-02-03T10:53:18Zcom_10630_2254col_10630_37953

Ruiz-Beltrán, Camilo Andrés Romero-Garces, Adrian González-García, Martín Marfil-Robles, Rebeca Bandera-Rubio, Antonio Jesús 2024-02-08T10:25:39Z 2024-02-08T10:25:39Z 2023-11-20 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. https://hdl.handle.net/10630/30091 10.3390/electronics12224713 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). eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Iris (Anatomía) - Reconocimiento Redes neuronales (Informática) FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance System. journal article