Abstract
In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.
Highlights
IntroductionIn order to determine the features which are necessary for person identification by the iris image, the image segmentation must be performed
Iris recognition is one of the most accurate methods of biometric identification
The results of the proposed method for the images from the CASIA IrisV4 database are presented in Fig. 8 and Fig. 9
Summary
In order to determine the features which are necessary for person identification by the iris image, the image segmentation must be performed. It includes determination of the inner and outer boundaries of the iris (iris localization) and the areas where eyelids and eyelashes overlap an eye. The result of segmentation is a mask, which is a binary image of the visible iris region. Neural network-based methods give usually better results than mathematical methods. The use of a hybrid method improves the results of CNN application. To obtain iris mask image two UNet-based convolutional neural networks are used. The modified principal curvatures method is used to combine predicted masks and to detect eyelashes correctly. Test results using CASIA IrisV4 Interval database [13] show the effectiveness of the proposed iris segmentation method
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