An approach to human iris recognition using quantitative analysis of image features and machine learning
This work addresses human identification using iris patterns, but it is incremental as it combines existing methods without introducing a new paradigm.
The paper tackled iris recognition by proposing a four-step framework involving segmentation, feature extraction, reduction, and classification, achieving an accuracy of 99.64% on the CASIA-Iris-Interval dataset.
The Iris pattern is a unique biological feature for each individual, making it a valuable and powerful tool for human identification. In this paper, an efficient framework for iris recognition is proposed in four steps. (1) Iris segmentation (using a relative total variation combined with Coarse Iris Localization), (2) feature extraction (using Shape&density, FFT, GLCM, GLDM, and Wavelet), (3) feature reduction (employing Kernel-PCA) and (4) classification (applying multi-layer neural network) to classify 2000 iris images of CASIA-Iris-Interval dataset obtained from 200 volunteers. The results confirm that the proposed scheme can provide a reliable prediction with an accuracy of up to 99.64%.