Real-Time Multi-Spectral Iris Extraction in Diversified Eye Images Utilizing Convolutional Neural Networks

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Abstract
Iris extraction has gained prominence due to its application versatility across many domains. However, achieving real-time iris extraction poses challenges due to several factors. While non-learning-based iris extraction methods are cost-effective and resource-efficient, they are outperformed by learning-based algorithms, which deliver superior accuracy and performance. In response, this article proposes a Convolutional Neural Networks (CNN)-based accurate direct iris extraction mechanism for a broad spectrum of eye images. The innovation of our approach lies in its proficiency with varied image types, including those where the iris is partially obscured by the eyelid. We enhance the method’s reliability by introducing a modified Circular Hough Transform (CHT). Extensive testing demonstrates our method’s excellent real-time performance across diverse image types, even under challenging conditions. These findings underscore the proposed method’s potential as a cost-effective and computationally efficient solution for real-time iris extraction in varied application domains.
Funding Information
  • Science and Technology Human Resource Development Project, Ministry of Education, Sri Lanka
  • Asian Development Bank (STHRD/CRG/R1/SJ/06)

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