2026-06-03T05:27:17Zhttps://riuma.uma.es/rest/oai/request

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

00925njm 22002777a 4500 dc Ruiz-Beltrán, Camilo Andrés author Romero-Garces, Adrian author González-García, Martín author Marfil-Robles, Rebeca author Bandera-Rubio, Antonio Jesús author 2023-11-20 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). 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 Iris (Anatomía) - Reconocimiento Redes neuronales (Informática) FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance System.