Improving iris image segmentation in unconstrained environments using NMF-based approach
Andri Santoso, Shabrina Choirunnisa, Bima Prihasto, Jia‐Ching Wang · 2016
Nowadays the segmentation task becomes an important pre-processing stage for the iris classification system. The earlier works in the iris classification field demonstrate a promising result when the classification is performed under an ideal environment. However, the reduction of accuracy is observed when the iris images are captured in non-ideal circumstances. This work is based on the previous work that propose iris segmentation system with ί-Means clustering algorithm. In this work, we evaluate the performance of NMF-based clustering approach to replace the ί-Means algorithm. The iris images from UBIRIS dataset are used to verify the reliability of our work to perform iris region extraction in the unconstrained environments.