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Deep Domain Adaption for Convolutional Neural Network (CNN) based Iris Segmentation: Solutions and Pitfalls
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Datum
2019
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Gesellschaft für Informatik e.V.
Zusammenfassung
Addressing 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.