Blood Vessel Segmentation for IRIS in Unconstrained Environments using Moment Method
|International Journal of Computer Science and Engineering|
|© 2018 by SSRG - IJCSE Journal|
|Volume 5 Issue 8|
|Year of Publication : 2018|
|Authors : Utkarsh Chouhan, H N Verma|
Utkarsh Chouhan, H N Verma, "Blood Vessel Segmentation for IRIS in Unconstrained Environments using Moment Method" SSRG International Journal of Computer Science and Engineering 5.8 (2018): 8-14.
Utkarsh Chouhan, H N Verma,(2018). Blood Vessel Segmentation for IRIS in Unconstrained Environments using Moment Method. SSRG International Journal of Computer Science and Engineering 5(8), 8-14.
In recent years the use of smart technology is increasing day by day and also to provide security to such devices biometric identification and recognition plays an important role. In biometric identification the one most efficient and reliable technique is found to Iris Recognition (IR). Previous iris recognition system (IRS) is restricted to focused on images acquired in limited environments likewise in laboratory for research. But, with the adaption of technology the scenario is changed. In this research, proposed Iris recognition using blood vessel segmentation (bvs). In preprocessing process, the iris image is improved using Adaptive Median Filter (AMF). After the bvs process, the segmented iris image is recognized using moment features. For feature extraction process the technique used is Standard Deviation (SD), Kurtosis, Skewness, Smoothness, Variance and Root Mean Square (RMS). For training process, extracted features are classified using well known Support Vector Machine (SVM) classifier. The performance of proposed work is evaluated using High Resolution Fundus (HRF) Image Database. The performance of proposed features is better as compared to previous work. The proposed IR approach is more secure and robust against blood vessel segmentation and has the ability to identify retinal images from the iris photograph images. Also the proposed result is more efficient in terms of accuracy as well as time complexity.
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Biometric recognition; Iris Recognition; Iris segmentation; Accuracy; Adaptive Median Filter; Blood Vessel.