| | 講 題 | An Iris Segmentation Algorithm using Faster RCNN | | | 講 者 | Department of Computer Science and Information Engineering , National Central University Yun Juan | | | 日 期 | 2018/10/24 | 長 度 | 00:08:06 | 人 氣 | 126 次 | | | Iris segmentation is a critical step in the entire iris recognition
procedure. Most of the state-of-the-art iris segmentation
algorithms are based on edge information. However, a large
number of noisy edge points created by a normal edge-based
detector in an image with specular reflection or other obstacles will
mislead the pupillary boundary and limbus boundary localization.
In this paper, we present a combination method of learning-based
and edge-based algorithms for iris segmentation. A well-designed
Faster R-CNN with only six layers is built to locate and classify the
eye. With the bounding box found by Faster R-CNN, the pupillary
region is located using a Gaussian mixture model. Then, the
circular boundary of the pupillary region is fit according to five
key boundary points. The enhanced version of MIGREP and a
boundary point selection algorithm [4] are used to find the
boundary points of limbus, and the circular boundary of limbus is
constructed using these bounding points. Experimental results
showed that the proposed iris segmentation method achieved
95.49% accuracy on the challenging CASIA-Iris-Thousand
database.
| | | 提 供 | TANET台灣網際網路研討會-TANET2018 | | |
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