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Human iris classification remains an active research area in the fields of biometrics as well as computer vision. In iris biometrics, most of the visible or near-infrared (NIR) eye images suffer from multiple noise sources, and the dispersive spectrum changes hugely. These changes occur due to spattering, albedo, and spectrum absorbance selectively. However, accurate iris classification for distance images is still a challenging task. To solve it effectively, we propose a machine learning (ML)-based iris classification employing a dense feature extraction method with various distance metrics. More specifically, this learning model focuses on the Histogram of Oriented Gradients (HOG) descriptor and K-Nearest Neighbour (K-NN) classifier with various distance metrics. The HOG descriptor has some advantages for this proposed distant-based iris classification, for example, insensitive to multiple lighting and noises, shift invariance, capacity to tolerate iris variations within the classes, etc. Additionally, this study investigates the most reliable distance metric that is less affected by different levels of noise. A publicly accessible CASIA-V4 distance image database is conducted for the experimental evaluation. To evaluate the performance of the classification models, we consider different measures such as recall, precision, F1-scores, and accuracy. The reported results are tabulated as well as optimized through Receiver Operating Characteristic (ROC) curves. The experimental results demonstrate that the Canberra distance metric with low dimensional HOG features provides better recognition accuracy (90.55%) compared to other distance metrics.
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