Visible Spectral Iris Segmentation via Deep Convolutional Network

He, Yuqing; Wang, Saijie; Pei, Kuo; Liu, Mingqi; Lai, Jiawei

doi:10.1007/978-3-319-69923-3_46

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…e segmentation accuracy of the proposed method was 95.49%, which was higher than the accuracy of 47.84% achieved in the previous work, and the time cost of the proposed iris segmentation procedure was only approximately 0.06 s. e results on the challenging CASIA-Iris-ousand database showed that the proposed method is a fast and accurate iris segmentation algorithm. e main advantage of the proposed algorithm over most of the state-of-the-art iris segmentation algorithms based on neural networks such as IrisDenseNet [55] and the model proposed by He et al [56] is that it has a smaller model size which make it faster to segment iris images, which is crucial for a real-time iris recognition system or even implement it on a mobile device. For the future work, we want to further improve the speed of the algorithm by creating heterogeneous models that combining the power of CNN and the speed of traditional computer vision methods.…”

Section: Resultsmentioning

confidence: 99%

“…ere are many iris segmentation methods based on deeply learned neural networks. In this section, we discuss the difference between two state-of-the-art methods, IrisDenseNet [55] and the model proposed by He et al in [56].…”

Section: Difference Between the Proposed Methods And Other Published Mmentioning

confidence: 99%

“…Model proposed in [56] also employs VGG-16 network but with some changes. Its execution time for one image is 0.112 second on a 2.6 GHz CPU and GTX970 M GPU which, again, is not fast enough for a real-time iris recognition system on the embedded system.…”

Section: Difference Between the Proposed Methods And Other Published Mmentioning

confidence: 99%

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An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Huang

Juan

2019

Mobile Information Systems

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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 detected 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. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.

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Section: Resultsmentioning

confidence: 99%

Section: Difference Between the Proposed Methods And Other Published Mmentioning

confidence: 99%

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An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

Huang

Juan

2019

Mobile Information Systems

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“…For these reasons, researchers should strive to improve the speed of the techniques by designing fused models that combine the strength of CNN techniques with the speed of traditional techniques of computer vision. Another way is to try to utilize the semantic segmentation 20 Complexity approach and combine it with the state-of-the-art iris segmentation techniques relying on neural networks like Iris-DenseNet [40] or Faster R-CNN technique as proposed by Girshick [278] or the techniques suggested by He et al [169] and Li et al [177]. e semantic segmentation techniques have a high sensibility of foretelling the reflection pixels in the iris region, which enhances the overall efficiency of the technique.…”

Section: Discussionmentioning

confidence: 99%

“…Despite that, there were limitations, for example, the non-iris pixels similar to the pixels of the iris region can be incorrectly detected as iris pixels [204]. To address that, He et al [169] introduced an iris segmentation method based on a deep CNN to extract the eye features and segment the iris, pupil, and sclera. e structure of CNN is a modified version of the DeepLab model [205].…”

Section: Other Cnn Architecture-based Modelsmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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