Name
Abstract | ||
|---|---|---|
Iris segmentation plays a vital role in the iris recognition system. However, it faces many challenges in non-ideal situations. To improve the iris segmentation performance for possible mobile devices, this paper presents a light iris segmentation method based on fully convolutional network. Firstly, a lightweight fully convolutional iris segmentation network is developed. Secondly, we adopt weighted loss, multi-level feature dense fusion module, multi-supervised training of multi-scale image and generative adversarial network to improve the segmentation performance. The final model is 6.21 M. Experiments show that the proposed method achieves 99.30% PA, 95.35% mIoU on UBIRIS.v2 and 99.66% PA, 96.75% mIoU on CASIA-Iris-Thousand database, which is relatively encouraging for a light iris segmentation network. It takes 41.56 ms and 63.03 ms to segment an image of UBIRIS.v2 and CASIA-Iris-Thousand databases, respectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00371-021-02134-1 | The Visual Computer |
Keywords | DocType | Volume |
Iris recognition, Iris segmentation, Fully convolutional network, Generative adversarial network | Journal | 38 |
Issue | ISSN | Citations |
7 | 0178-2789 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Wang | 1 | 0 | 0.34 |
| Xiangyue Meng | 2 | 0 | 0.34 |
| Ting Sun | 3 | 39 | 12.08 |
| Xiangde Zhang | 4 | 91 | 15.32 |