Gaze-angle Impact on Iris Segmentation using CNNs
Ehsaneddin Jalilian, Andreas Uhl, Mahmut Özge Karakaya · 2019
Emerging standoff iris recognition systems operate under unconstrained conditions and the iris images captured by these systems are more subject to off-angle acquisition distortions. While deep learning techniques (e.g. convolutional neural networks (CNNs)) are increasingly becoming a tool of choice for iris segmentation tasks, yet there is a significant lack of information about how these distortions affect the performance of such networks. In this work, we thoroughly discuss the general effect of different gaze-angles on ocular biometrics and relate the findings to off-angle iris segmentation using CNNs. In particular, we conduct systematical analysis on the impact of different gaze-angles on segmentation performance of two CNNs with different architectures. The networks' performance turns out to have a direct relation to the closeness of gaze-angles in the training and testing images, and it declines as the gaze-angles diverge. We further investigate the effect of (i) increasing the quantity of iris training data in case of gaze-angles in training and test data match, and (ii) considering iris training data consisting of several distinct gaze-angles (we obtain promising results using the second configuration). Finally, we compare our results to those of some classical iris segmentation algorithms, where the CNNs are found to outperform the classical algorithms.