Jalilian, EhsaneddinUhl, AndreasBrömme, ArslanBusch, ChristophDantcheva, AntitzaRathgeb, ChristianUhl, Andreas2020-09-152020-09-152019978-3-88579-690-9https://dl.gi.de/handle/20.500.12116/34239Addressing the lack of massive amounts of labeled training data, deep domain adaptation has been applied successfully in many applications of machine learning. We investigate the application of deep domain adaptation for CNN based iris segmentation, exploring available solutions and their corresponding strengths and pitfalls, with several major contributions. First, we provide a comprehensive survey of current deep domain adaptation methods according to the properties of data that cause the domains divergence. Second, after selecting credible methods, we evaluate their expedience in terms of iris segmentation performance. Third, we analyze and compare the performance against the state-of-the-art methods under these categories. Forth, potential shortfalls of current methods and several future directions are pointed out and discussed.enDeep domain adaptationiris segmentationconvolutional neural networks.Text/Conference Paper1617-5468