Difference between revisions of "Iris Segmentation Groundtruth"
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| − | 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. | + | 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. However, the overall recognition performance is not only affected by the segmentation accuracy, but also by the performance of the other subsystems based on possible suboptimal segmentation of the iris. As such it is difficult to differentiate between defects in the iris segmentation system and effects which might be introduced later in the system. |
| − | For these reasons, with the purpose of allowing assessment of iris segmentation algorithms with independence of the whole biometric system, we have generated an iris segmentation ground truth database. We have segmented a total of | + | For these reasons, with the purpose of allowing assessment of iris segmentation algorithms with independence of the whole biometric system, we have generated an iris segmentation ground truth database. We have segmented a total of 12,621 iris images from 7 databases. This data is now made publicly available, and can be used to analyse existing and test new iris segmentation algorithms. |
| − | + | The iris segmentation database (IRISSEG) contains a mask for each iris image in form of parameters and a method to extract the mask. The database is partitioned into two datasets | |
| + | based on the shapes used for segmenting the iris and eyelid, the CC and EP dataset. For the CC dataset the parameters define circles which give the iris boundaries and eyelid | ||
| + | maskings. For the EP dataset the parameters define ellipses for the iris and polynomials for the eyelid. Note that the eyelid parametrization was done in a way to ensure the best possible separation of iris and eyelids in the iris region, i.e. outside the iris region the parametrization is not necessarily accurate. Eyelashes information is not included in the segmentation data. | ||
| − | This | + | This page describes the CC dataset (IRISSEG-CC Dataset), which has been generated by Halmstad University. The EP dataset has been generated by the University of Salzburg, and it can be obtained [http://www.wavelab.at/sources/irisseg-ep/ here] |
| − | + | Only iris segmentation data is provided the IRISSEG dataset, not the original eye image databases, since they are not owned by us. A link to the actual iris databases is included in each case, please refer to them in order to obtain the original databases. | |
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| + | == Database of iris segmentation ground truth (IRISSEG-CC Dataset) == | ||
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| + | The parameters given in this dataset define circles (centre and radius) which give the iris boundaries and eyelid masks. Three points of each circle have been manually marked by an operator, which are used to compute the corresponding radius and centre. An example is as follows: | ||
[[Image:sample_mask.jpg|400px]] | [[Image:sample_mask.jpg|400px]] | ||
| − | + | ||
=== References === | === References === | ||
Revision as of 12:11, 28 April 2014
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. However, the overall recognition performance is not only affected by the segmentation accuracy, but also by the performance of the other subsystems based on possible suboptimal segmentation of the iris. As such it is difficult to differentiate between defects in the iris segmentation system and effects which might be introduced later in the system.
For these reasons, with the purpose of allowing assessment of iris segmentation algorithms with independence of the whole biometric system, we have generated an iris segmentation ground truth database. We have segmented a total of 12,621 iris images from 7 databases. This data is now made publicly available, and can be used to analyse existing and test new iris segmentation algorithms.
The iris segmentation database (IRISSEG) contains a mask for each iris image in form of parameters and a method to extract the mask. The database is partitioned into two datasets based on the shapes used for segmenting the iris and eyelid, the CC and EP dataset. For the CC dataset the parameters define circles which give the iris boundaries and eyelid maskings. For the EP dataset the parameters define ellipses for the iris and polynomials for the eyelid. Note that the eyelid parametrization was done in a way to ensure the best possible separation of iris and eyelids in the iris region, i.e. outside the iris region the parametrization is not necessarily accurate. Eyelashes information is not included in the segmentation data.
This page describes the CC dataset (IRISSEG-CC Dataset), which has been generated by Halmstad University. The EP dataset has been generated by the University of Salzburg, and it can be obtained here
Only iris segmentation data is provided the IRISSEG dataset, not the original eye image databases, since they are not owned by us. A link to the actual iris databases is included in each case, please refer to them in order to obtain the original databases.
Contents
[hide]Database of iris segmentation ground truth (IRISSEG-CC Dataset)
The parameters given in this dataset define circles (centre and radius) which give the iris boundaries and eyelid masks. Three points of each circle have been manually marked by an operator, which are used to compute the corresponding radius and centre. An example is as follows:
References
Please remember to cite the following references on any work made public based directly or indirectly on the IRISSEG-CC Dataset (do not forget also to cite the appropriate publications of the original eye image databases, as indicated by their owners):
- Heinz Hofbauer, Fernando Alonso-Fernandez, Peter Wild, Josef Bigun and Andreas Uhl, “A Ground Truth for Iris Segmentation”, Proc. 22nd International Conference on Pattern Recognition, ICPR, Stockholm, August 24-28, 2014 (link to the publication)
Databases available (IRISSEG-CC Dataset)
Biosec baseline database (iris part)
The BioSec database has 3,200 iris images of 640x480 pixels from 200 subjects acquired with a LG IrisAccess EOU3000 close-up infrared iris camera. Here, we use a subset comprising data from 75 subjects (totalling 1,200 iris images), for which iris and eyelids segmentation groundtruth is available.
Link to the original database: click here
Ground truth segmentation files:
Casia Iris v3 Interval database
The CASIA-Iris-Interval subset of the CASIA v3.0 database, containing 2655 iris images of 320x280 pixels from 249 subjects, was fully segmented. Images were acquired with a close-up infrared iris camera in an indoor environment, having images with very clear iris texture details thanks to a circular NIR LED array.
Link to the original database: click here
Ground truth segmentation files:
MobBIO database (iris train dataset)
The iris training subset of the MobBIO database, containing 800 images of 240x200 pixels from 100 subjects, was fully segmented. Images were captured with the Asus Eee Pad Transformer TE300T Tablet (webcam in visible light) in two different lightning conditions, with variable eye orientations and occlusion levels, resulting in a large variability of acquisition conditions. Distance to the camera was kept constant, however.
Link to the original database: click here
Ground truth segmentation files:
Request password of the ground truth segmentation files to: Fernando Alonso-Fernandez
