Paper Info
Title
Pfsegiris: Precise And Fast Segmentation Algorithm For Multi-Source Heterogeneous Iris
Abstract
Current segmentation methods have limitations for multi-source heterogeneous iris segmentation since differences of acquisition devices and acquisition environment conditions lead to images of greatly varying quality from different iris datasets. Thus, different segmentation algorithms are generally applied to distinct datasets. Meanwhile, deep-learning-based iris segmentation models occupy more space and take a long time. Therefore, a lightweight, precise, and fast segmentation network model, PFSegIris, aimed at the multi-source heterogeneous iris is proposed by us. First, the iris feature extraction modules designed were used to fully extract heterogeneous iris feature information, reducing the number of parameters, computation, and the loss of information. Then, an efficient parallel attention mechanism was introduced only once between the encoder and the decoder to capture semantic information, suppress noise interference, and enhance the discriminability of iris region pixels. Finally, we added a skip connection from low-level features to catch more detailed information. Experiments on four near-infrared datasets and three visible datasets show that the segmentation precision is better than that of existing algorithms, and the number of parameters and storage space are only 1.86 M and 0.007 GB, respectively. The average prediction time is less than 0.10 s. The proposed algorithm can segment multi-source heterogeneous iris images more precisely and quicker than other algorithms.
Year
DOI
Venue
2021
10.3390/a14090261
ALGORITHMS
Keywords
DocType
Volume
iris segmentation, heterogeneous iris, fast segmentation, iris recognition
Journal
14
Issue
Citations 
PageRank 
9
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Lin Dong1258.55
Yuanning Liu200.34
Xiaodong Zhu37310.24