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Updated: Jul 16, 2025

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Real-Time Embedded Eye Image Defocus Estimation for Iris Biometrics.

Camilo A Ruiz-Beltrán1, Adrián Romero-Garcés1, Martín González-García1

  • 1Departamento Tecnologia Electronica, ETSI Telecomunicacion, University of Málaga, 29071 Málaga, Spain.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
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This study introduces an efficient eye image detection system for iris recognition. It effectively discards out-of-focus images, improving system performance and reducing processing load.

Area of Science:

  • Computer Vision
  • Biometrics
  • Embedded Systems Engineering

Background:

  • Iris recognition systems face challenges with non-cooperative subjects at a distance.
  • Achieving less intrusive iris recognition requires wide field-of-view sensors and high frame rates.
  • Low image quality, particularly defocus blur, hinders reliable iris recognition in real-world scenarios.

Purpose of the Study:

  • To implement an eye image detection system on a multiprocessor system-on-chip (MPSoC).
  • To develop a hardware block for evaluating defocus blur in captured eye images.
  • To enhance iris recognition system efficiency by pre-filtering low-quality images.

Main Methods:

  • Implementation of an eye image detection system on the programmable logic (PL) of an MPSoC.
Keywords:
Haar-like featuresUltrascale+ MP SoCconvolution kernelsdefocus testeye detection

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  • Integration of a functional block using Vitis High Level Synthesis (VHLS) for defocus blur evaluation.
  • Processing of images from a 16 Mpixel sensor at over 57 frames per second (fps).

    • Main Results:

    • The system successfully discards unfocused eye images, validated on the CASIA-Iris-distance V4 database.
    • The proposed framework achieves high-speed eye detection with a 16 Mpixel sensor.
    • In real-world implementations, up to 97% of out-of-focus images are discarded before further processing.

    Conclusions:

    • The developed MPSoC-based system effectively identifies and discards out-of-focus eye images.
    • This approach significantly reduces the processing burden on subsequent iris recognition stages.
    • The system enhances the practicality and efficiency of iris recognition in challenging, non-cooperative scenarios.