Kakinada Institute of Engineering and Technology for Women
Abstract
The significance of Iris detection and recognition in area of bioinformatics and pattern recognition has been increased from last few decades. Looking at the importance of Iris detection and recognition, we propose a robust, stable and reliable computational model. Features are extracted from iris images using two different approaches such as Hilbert transform and Fast wavelet Hadamard Transform (FWHT).Random forest is used as a classification algorithm. 5-folds cross validation test is applied to evaluate the performance of K-nearest neighbor. Among three feature spaces, FWHT feature space has achieved promising results. The success rate of K-nearest neighbor on FWHT feature space is 94.4%. After examining the results, we have observed that our model might be useful and helpful for iris detection in future work
