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Development of Iris Image Classification Framework using Multi-Layer CNN Architecture

Author

Listed:
  • R. D. Bhoyar

    (Department of Computer Science, SGB Amravati University, Amravati)

  • D. R. Solanke

    (Department of Applied Electronics, SGB Amravati University, Amravati)

  • S. D. Pachpande

    (Department of Computer Science, SGB Amravati University, Amravati)

Abstract

This paper proposes a multi-layer Convolutional Neural Network (CNN) framework for iris image classification, targeting left and right eye recognition across 46 subjects. A custom five-layer CNN was trained for 200 epochs with a learning rate of 0.0001, effectively learning discriminative features from iris textures. The model achieved a training accuracy of 97.90% with a loss of 0.4116, and a testing accuracy of 93.09% with a loss of 0.6837, demonstrating robust generalization to unseen data. The results highlight the potential of multi-layer CNN architectures for reliable iris-based biometric systems, enabling accurate and automated eye classification. The key contribution of this work is the demonstration that a compact five-layer CNN can achieve high accuracy in binary left-right iris classification, offering an efficient and scalable solution for biometric authentication.

Suggested Citation

  • R. D. Bhoyar & D. R. Solanke & S. D. Pachpande, 2025. "Development of Iris Image Classification Framework using Multi-Layer CNN Architecture," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(11), pages 459-467, November.
    • Handle: RePEc:bjf:journl:v:10:y:2025:i:11:p:459-467
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