An Efficient Framework for Iris Segmentation with additional Pressure term in the Active Contour model and Classification

In this digital era, safety plays a vital role in all our day-to-day applications. In human identification systems, iris patter recognition is statistically unique and also pattern recognition is used for iris based person identification. However, the segmentation of the iris image still remains a bottleneck. In this proposed framework, a pupil and Gradient Vector Flow (GVF) model with additional pressure term in the external force of the active contour is used for segmentation. The HOG and Gabor Filter are employed for feature extraction. The proposed system performs better for the combined features of HOG and Gabor Filter as computed individually. When compared with other methods, the proposed system shows the effect in terms of classification accuracy. In the planned method, the MMU database is used to evaluate the performance.