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Performance Analysis of Textured Contact Lens IRIS Detection Based on Manual Feature Engineering

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Advances in Intelligent Computing Techniques and Applications (IRICT 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 211))

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Abstract

Numerous methods exist for identifying a person using their biometric characteristics. IRIS detection systems are one of these traits. Current iris identification systems are prone to iris presentation attacks. The most challenging to spot of the several iris presentation attacks is probably the use of textured contact lenses. No specialized survey concentrating on IRIS detection, particularly Contact Lenses Iris Detection Algorithms (CLIDs), has been published in the previous five years. Therefore, the paper reviewed recent CLID algorithms-based hand-crafted features, which were grouped into two categories: CLIDs-based spatial domain features, and CLIDs-based transform domain Features. CLIDs-based hand-crafted features are techniques that use human feature extraction to detect a counterfeit IRIS image. The performance of various current CLID algorithms based on traditional Features is compared. Finally, we hope that our review has encapsulated the majority of recent CLID studies.

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Authors and Affiliations

  1. College of Computer Sciences and Information Technology, University of Anbar, Ramadi, Iraq

    Roqia Sailh Mahmood & Ismail Taha Ahmed

Authors
  1. Roqia Sailh Mahmood
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  2. Ismail Taha Ahmed
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Correspondence to Ismail Taha Ahmed .

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Editors and Affiliations

  1. School of Computing and Digital Techn., Birmingham City University, BIRMINGHAM, UK

    Faisal Saeed

  2. Department of Business Analytics, Sunway Business School, Sunway University, Selangor, Malaysia

    Fathey Mohammed

  3. Department of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV, USA

    Yousef Fazea

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Mahmood, R.S., Ahmed, I.T. (2024). Performance Analysis of Textured Contact Lens IRIS Detection Based on Manual Feature Engineering. In: Saeed, F., Mohammed, F., Fazea, Y. (eds) Advances in Intelligent Computing Techniques and Applications. IRICT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-031-59707-7_18

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