An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition

Iris recognition refers to identifying individuals based on iris patterns, which have been widely used in security systems, such as subway security and access control attendance, because everyone has a unique iris shape. In the study, we propose an OCaNet model for the iris recognition task. First,...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2021-10, Vol.2021, p.1-10
Main Authors: Zou, Dong, Feng, Jianbing, He, Zhixin, Liu, Liping, Zhao, Meijun, Zheng, Lizhong
Format: Article
Language:English
Subjects:
Access control
Accuracy
Algorithms
Biometric recognition systems
Biometrics
Classification
Convolution
Coordinate transformations
Deep learning
Feature extraction
Feature maps
Hough transformation
Image segmentation
Methods
Modules
Neural networks
Object recognition
Security systems
Shape
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Iris recognition refers to identifying individuals based on iris patterns, which have been widely used in security systems, such as subway security and access control attendance, because everyone has a unique iris shape. In the study, we propose an OCaNet model for the iris recognition task. First, binarized threshold segmentation is used to locate the pupil and the pupil boundary is obtained; then, the Hough transform is applied to locate the outer edge of the iris; according to the located pupil and iris, the iris area image is obtained through image segmentation; finally, the iris image is normalized to adjust each original image to the same size and corresponding position, so as to eliminate the influence of translation, scaling, and rotation on iris recognition. Second, the normalized iris images are both input into the octave convolution module and attention module. The octave convolution module is used to extract the shape and contour features of the iris by decomposing the feature map into high and low frequencies. The attention module is applied to extract the color and texture characteristics of the iris. Finally, the two feature maps are concatenated and produce a distribution of output classes. Experimental results show that the proposed OCaNet model is significantly more accurate.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/3412060