Work

Real-Time Detection and Segmentation of the Iris At A Distance Scenarios Embedded in Ultrascale MPSoC

Year: 2025

Type: article

Abstract: Iris recognition is currently considered the most promising biometric method and has been applied in many fields. Current commercial and research systems typically use software solutions running on a dedicated computer, whose power consumption, size and price are considerably high. This paper presents a hardware-based embedded solution for real-time iris segmentation. From an algorithmic point of view, the system consists of two steps. The first employs a YOLOX trained to detect two classes: eyes and iris/pupil. Both classes intersect in the last of the classes and this is used to emphasise the detection of the iris/pupil class. The second stage uses a lightweight U-Net network to segment the iris, which is applied only on the locations provided by the first stage. Designed to work in an Iris At A Distance (IAAD) scenario, the system includes quality parameters to discard low-contrast or low-sharpness detections. The whole system has been integrated on one MultiProcessor System-on-Chip (MPSoC) using AMD’s Deep learning Processing Unit (DPU). This approach is capable of processing the more than 45 frames per second provided by a 16 Mpx CMOS digital image sensor. Experiments to determine the accuracy of the proposed system in terms of iris segmentation are performed on several publicly available databases with satisfactory results.

Source: Electronics

DOI: 10.3390/electronics14183698

Authors: Camilo Ruiz-Beltrán, Óscar Pons, Martín González-García, Antonio Bandera

Institution: Universidad de Málaga

Language: English

Topic: Biometric Identification and Security

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: European Commission, Chinese Academy of Sciences, European Regional Development Fund, Agencia Estatal de Investigación