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Abstract <p>Iris pattern recognition’s stability and uniqueness have significantly improved biometric authentication. The individual’s iris is the most promising biometric authentication method that can reliably identify an individual based on their unique characteristics. Traditional drawback of iris recognition systems is their vulnerability to such spoofing, which can compromise the effectiveness of biometric security, making it essential to enhance the detection of fraudulent attempts by the system. Iris recognition problems still exist even with sophisticated algorithms, especially in real-world settings where consumers might not be aware of any weaknesses. A deep learning-based segmentation and classification method for iris liveness detection utilizing quality metrics has been proposed in order to address this problem. Human eye images are first gathered and used as an input dataset. A Lightweight Attention Guided ConvNext Network (LACN) is used for image improvement and a Pixel Density based Trimmed Median Filter (PDTMF) to reduce noise. A Lightweight Dual Multiscale Residual Block-based Convolutional Neural Network (LDMRes-Net) is used for segmentation in order to recover the iris and pupil regions. These separated regions are used to calculate important iris and pupil region characteristic<b>s</b> including as area, radius, and perimeter. Additionally, quality measures including pupil-iris ratio, iris-sclera contrast, iris-pupil contrast, grayscale utilization, and usable iris area are calculated. A deep learning classifier known as the Progressively Growing Adversarial Network with Dropout Layer (PGAN-DL) uses to determine iris liveness. Simulated findings show that the proposed technique achieves 98.1% accuracy, 97.2% PPV, and 6.2% miss rate for Pupil and 95.6% accuracy, 96.8% PPV, and 93.8% hit rate for Iris. Thus, the proposed approach is the most effective strategy for identifying iris liveness using quality criteria.</p>

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Deep Learning Based Segmentation and Classification for Effective Iris Liveness Detection Using Various Quality Metrics

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Vaishali C. Kulloli,
  • Maheshwari S. Biradar

摘要

Abstract

Iris pattern recognition’s stability and uniqueness have significantly improved biometric authentication. The individual’s iris is the most promising biometric authentication method that can reliably identify an individual based on their unique characteristics. Traditional drawback of iris recognition systems is their vulnerability to such spoofing, which can compromise the effectiveness of biometric security, making it essential to enhance the detection of fraudulent attempts by the system. Iris recognition problems still exist even with sophisticated algorithms, especially in real-world settings where consumers might not be aware of any weaknesses. A deep learning-based segmentation and classification method for iris liveness detection utilizing quality metrics has been proposed in order to address this problem. Human eye images are first gathered and used as an input dataset. A Lightweight Attention Guided ConvNext Network (LACN) is used for image improvement and a Pixel Density based Trimmed Median Filter (PDTMF) to reduce noise. A Lightweight Dual Multiscale Residual Block-based Convolutional Neural Network (LDMRes-Net) is used for segmentation in order to recover the iris and pupil regions. These separated regions are used to calculate important iris and pupil region characteristics including as area, radius, and perimeter. Additionally, quality measures including pupil-iris ratio, iris-sclera contrast, iris-pupil contrast, grayscale utilization, and usable iris area are calculated. A deep learning classifier known as the Progressively Growing Adversarial Network with Dropout Layer (PGAN-DL) uses to determine iris liveness. Simulated findings show that the proposed technique achieves 98.1% accuracy, 97.2% PPV, and 6.2% miss rate for Pupil and 95.6% accuracy, 96.8% PPV, and 93.8% hit rate for Iris. Thus, the proposed approach is the most effective strategy for identifying iris liveness using quality criteria.