Jalilian,EhsaneddinUhl,AndreasKwitt,RolandBrömme,ArslanBusch,ChristophDantcheva,AntitzaRathgeb,ChristianUhl,Andreas2017-09-262017-09-262017978-3-88579-664-0https://dl.gi.de/handle/20.500.12116/4663Convolutional Neural Networks (CNNs) have shown great success in solving key artificial vision challenges such as image segmentation. Training these networks, however, normally requires plenty of labeled data, while data labeling is an expensive and time-consuming task, due to the significant human effort involved. In this paper we propose two pixel-level domain adaptation methods, introducing a training model for CNN based iris segmentation. Based on our experiments, the proposed methods can effectively transfer the domains of source databases to those of the targets, producing new adapted databases. The adapted databases then are used to train CNNs for segmentation of iris texture in the target databases, eliminating the need for the target labeled data. We also indicate that training a specific CNN for a new iris segmentation task, maintaining optimal segmentation scores, is possible using a very low number of training samples.enDomain adaptationCNN based iris segmentationIris segmentation1617-5468