Abstract
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.
Highlights
Iris recognition has been widely used in many fields, especially in biometric pattern recognition [1]; at the same time, a lot of institutions and governments employ biometric technology due to its high accuracy over other human characteristics, such as handwriting and fingerprint [2]
With the development of deep learning, especially convolutional neural networks (CNNs), it has been widely applied in the task of object detection [6], image classification [7], and face recognition [8]; it has led to breakthroughs in iris recognition, which shows good performance in recognition and classification scenarios
The output of the feature maps extracted by the octave convolution module and attention module is concatenated and passed onto the fully connected layer, which produces a distribution of output classes. e network structural parameters are given in Table 1. e number of training epochs was generally kept constant at 300 epochs, learning rate is 1e-3, and the momentum is 0.9. e model is trained using the Adam optimizer [31]
Summary
Iris recognition has been widely used in many fields, especially in biometric pattern recognition [1]; at the same time, a lot of institutions and governments employ biometric technology due to its high accuracy over other human characteristics, such as handwriting and fingerprint [2]. With the development of deep learning, especially convolutional neural networks (CNNs), it has been widely applied in the task of object detection [6], image classification [7], and face recognition [8]; it has led to breakthroughs in iris recognition, which shows good performance in recognition and classification scenarios. Liu et al [12] applied CNNs to iris recognition for the first time; hierarchical convolutional neural networks and multiscale fully convolutional network were proposed for iris segmentation. Nguyen et al [16]
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