Publications:A Ground Truth for Iris Segmentation
From ISLAB/CAISR
| Title | A Ground Truth for Iris Segmentation |
|---|---|
| Author | Heinz Hofbauer and Fernando Alonso-Fernandez and Peter Wild and Josef Bigun and Andreas Uhl |
| Year | 2014 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | 2014 22nd International Conference on Pattern Recognition (ICPR) |
| Conference | 22nd International Conference on Pattern Recognition, ICPR, Stockholm, Sweden, August 24-28, 2014 |
| DOI | http://dx.doi.org/10.1109/ICPR.2014.101 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:710627 |
| Abstract | Classical iris biometric systems assume ideal environmental conditions and cooperative users for image acquisition. When conditions are less ideal or users are uncooperative or unaware of their biometrics being taken the image acquisition quality suffers. This makes it harder for iris localization and segmentation algorithms to properly segment the acquired image into iris and non-iris parts. Segmentation is a critical part in iris recognition systems, since errors in this initial stage are propagated to subsequent processing stages. Therefore, the performance of iris segmentation algorithms is paramount to the performance of the overall system. In order to properly evaluate and develop iris segmentation algorithm, especially under difficult conditions like off angle and significant occlusions or bad lighting, it is beneficial to directly assess the segmentation algorithm. Currently, when evaluating the performance of iris segmentation algorithms this is mostly done by utilizing the recognition rate, and consequently the overall performance of the biometric system. In order to streamline the development and assessment of iris segmentation algorithms with the dependence on the whole biometric system we have generated a iris segmentation ground truth database. We will show a method for evaluating iris segmentation performance base on this ground truth database and give examples of how to identify problematic cases in order to further analyse the segmentation algorithms. ©2014 IEEE. |
