  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%.
