An Enhanced Iris Recognition using Deep Learning and 2D Chebyshev Wavelet Filter
DOI:
https://doi.org/10.32890/jict2026.25.3.5Keywords:
Biometric authentication, deep learning, 2D Chebyshev wavelet, feature extraction, iris recognitionAbstract
Iris recognition is widely regarded as a reliable biometric authentication technique; however, its performance is often degraded by poor image quality, noise, eyelid and eyelash occlusion, illumination variation, off-angle acquisition, and partial iris visibility in unconstrained environments. This study proposes an enhanced iris recognition framework that integrates a Two-Dimensional Chebyshev Wavelet Filter (2DCWF) with a Convolutional Neural Network (CNN) to improve feature extraction, classification accuracy, and robustness under challenging imaging conditions. The proposed methodology consists of image acquisition from benchmark iris datasets, preprocessing through greyscale conversion, median filtering, and histogram equalisation, followed by iris segmentation using the Circular Hough Transform and normalisation using the Daugman rubber sheet model. The normalised iris images are then processed using the 2DCWF to extract discriminative multi-resolution spatial-frequency and texture features, which are supplied to a CNN classifier for identity recognition. The framework was evaluated using CASIA-IrisV4, UBIRIS.v2, and MMU datasets representing both controlled and unconstrained acquisition conditions. Experimental results show that the proposed 2DCWF-CNN model outperforms conventional feature extraction methods, including Gabor Wavelet Filter, Local Binary Pattern, and Legendre Wavelet Filter, achieving a highest recognition accuracy of 98.97%, with reduced False Acceptance Rate and False Rejection Rate and improved Genuine Acceptance Rate. These findings demonstrate that combining Chebyshev wavelet-based feature extraction with deep learning provides a robust and effective solution for reliable iris recognition in real-world biometric authentication systems.
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