Non-ideal iris segmentation using Polar Spline RANSAC and illumination compensation

Labati, Ruggero Donida; Muñoz, Enrique; Piuri, Vincenzo; Ross, Arun; Scotti, Fabio

doi:10.1016/j.cviu.2019.07.007

Cited by 14 publications

(2 citation statements)

References 86 publications

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“…In this way, the surface of the iris has uneven radially arranged and interlaced wrinkled walls, forming a large number of recesses. These high and low changes in the structure form a unique texture [25]. The iris is the internal tissue of a protected eye, behind the cornea and crystal fluid.…”

Section: Fig 1 Biological Characteristics Of the Irismentioning

confidence: 99%

Iris recognition method based on segmentation

Mahameed¹,

Ahmed²,

Abdullah³

2022

Eureka: PE

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The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studies

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Section: Fig 1 Biological Characteristics Of the Irismentioning

confidence: 99%

Iris recognition method based on segmentation

Mahameed¹,

Ahmed²,

Abdullah³

2022

Eureka: PE

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“…Mobile iris biometrics 1 has emerged as a reliable personal identification method and plays a vital role in many applications such as intelligent unlocking, mobile payments, banking, social networks, border crossing control, etc. However, less-constrained mobile environments, e.g., visible illumination, limited user cooperation, and at a distance, 2 , 3 could introduce many adverse noise factors, such as specular reflections, gaze deviation, and occlusion, as illustrated in Fig. 1, which can significantly deteriorate the performance of iris segmentation, and thus, in turn, affect the accuracy of iris recognition 1 , 4 …”

Section: Introductionmentioning

confidence: 99%

LiSeNet: multitask lightweight segmentation network for accurate and complete iris segmentation

Sun

Liu

et al. 2022

J. Electron. Imag.

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. The demand for applying the iris segmentation model on mobile devices has been growing rapidly. Most current segmentation networks have an enormous amount of parameters, hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification networks and ignore the inherent characteristic of segmentation. To address the challenge, we propose a lightweight segmentation network (LiSeNet) for iris segmentation of noisy images. Unlike previous studies that only focus on improving the accuracy of segmentation masks, LiSeNet can simultaneously obtain segmentation masks, parameterized pupillary and limbic boundaries of the iris, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We first propose a multiscale concatenate (MSC) Block, which connects multiple sizes of convolution kernels in a dense manner, gradually reduces the dimension of feature maps and uses the aggregation of them for image representation. Based on the MSC block, we develop a two-stage refinement encoder to aggregate discriminative features through subnetwork feature reuse and substage feature reassess, thus obtaining a sufficient receptive field and enhancing the model learning ability. To exploit object contextual information more efficiently, we further devise a grouped spatial attention to emphasize the important features and suppress irrelevant noises through a gating mechanism in the decoder. Extensive experiments on three challenging iris datasets show that LiSeNet, without any complicated postprocessing, achieves competitive or state-of-the-art performance with only 2.2M parameters, being 14 × smaller than the previous best method. Code will be publicly available.

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