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Auflistung nach Autor:in "Jalilian, Ehsaneddin"

1 - 2 von 2
  • Konferenzbeitrag
    Deep Domain Adaption for Convolutional Neural Network (CNN) based Iris Segmentation: Solutions and Pitfalls
    (BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Jalilian, Ehsaneddin; Uhl, Andreas
    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.
  • Konferenzbeitrag
    End-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Jalilian, Ehsaneddin; Karakaya, Mahmut; Uhl, Andreas
    While deep learning techniques are increasingly becoming a tool of choice for iris segmentation, yet there is no comprehensive recognition framework dedicated for off-angle iris recognition using such modules. In this work, we investigate the effect of different gaze-angles on the CNN based off-angle iris segmentations, and their recognition performance, introducing an improvement scheme to compensate for some segmentation degradations caused by the off-angle distortions. Also, we propose an off-angle parameterization algorithm to re-project the off-angle images back to frontal view. Taking benefit of these, we further investigate if: (i) improving the segmentation outputs and/or correcting the iris images before or after the segmentation, can compensate for off-angle distortions, or (ii) the generalization capability of the network can be improved, by training it on iris images of different gaze-angles. In each experimental step, segmentation accuracy and the recognition performance are evaluated, and the results are analyzed and compared.