CNN‐based off‐angle iris segmentation and recognition

Jalilian, Ehsaneddin; Karakaya, Mahmut; Uhl, Andreas

doi:10.1049/bme2.12052

Cited by 10 publications

(4 citation statements)

References 48 publications

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“…Jalilian et al [21] developed an algorithm that does not require additional information to estimate the angle under which the photo of the human eye was taken. The developed algorithm is based on the measurement of the relative distance of empirically determined characteristic points of the iris and the pupil.…”

Section: State Of the Artmentioning

confidence: 99%

An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

Malinowski

Saeed

2022

Biocybernetics and Biomedical Engineering

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Section: State Of the Artmentioning

confidence: 99%

An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

Malinowski

Saeed

2022

Biocybernetics and Biomedical Engineering

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“…MFCNs 20 took the entire iris image as input and then directly predicted the iris mask. Jalilian et al 33 discussed the general effect of different gaze angles on ocular biometrics and formulated several pipelines to configure an end-to-end framework for the CNN-based off-angle iris segmentation and recognition. Lu et al 34 proposed a contour-based model that combines the coarse and fine localization results for off-angle and occluded iris segmentation and recognition.…”

Section: Iris Segmentation and Localizationmentioning

confidence: 99%

“…MFCNs 20 took the entire iris image as input and then directly predicted the iris mask. Jalilian et al 33 . discussed the general effect of different gaze angles on ocular biometrics and formulated several pipelines to configure an end-to-end framework for the CNN-based off-angle iris segmentation and recognition.…”

Section: Related Workmentioning

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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“…Despite its high accuracy in controlled environments, iris recognition can be significantly affected by non-ideal scenarios, such as off-angle iris images [2]. While it is effective in identifying subjects with less than 20° gaze difference, its accuracy reduced at steeper gaze angles, possibly due to the difference in the elliptical shape of the irises between frontal and non-frontal gaze angles [3].…”

Section: Introductionmentioning

confidence: 99%

Off-angle iris recognition using single and multiple deep learning frameworks

Chavarro,

Karakaya

2023

Multimodal Image Exploitation and Learning 2023

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Iris recognition is a widely used biometric technology that has high accuracy and reliability in well-controlled environments. However, the recognition accuracy can significantly degrade in non-ideal scenarios, such as off-angle iris images. To address these challenges, deep learning frameworks have been proposed to identify subjects through their off-angle iris images. Traditional CNN-based iris recognition systems train a single deep network using multiple off-angle iris image of the same subject to extract the gaze invariant features and test incoming off-angle images with this single network to classify it into same subject class. In another approach, multiple shallow networks are trained for each gaze angle that will be the experts for specific gaze angles. When testing an off-angle iris image, we first estimate the gaze angle and feed the probe image to its corresponding network for recognition. In this paper, we present an analysis of the performance of both single and multimodal deep learning frameworks to identify subjects through their off-angle iris images. Specifically, we compare the performance of a single AlexNet with multiple SqueezeNet models. SqueezeNet is a variation of the AlexNet that uses 50x fewer parameters and is optimized for devices with limited computational resources. Multi-model approach using multiple shallow networks, where each network is an expert for a specific gaze angle. Our experiments are conducted on an off-angle iris dataset consisting of 100 subjects captured at 10-degree intervals between -50 to +50 degrees. The results indicate that angles that are more distant from the trained angles have lower model accuracy than the angles that are closer to the trained gaze angle. Our findings suggest that the use of SqueezeNet, which requires fewer parameters than AlexNet, can enable iris recognition on devices with limited computational resources while maintaining accuracy. Overall, the results of this study can contribute to the development of more robust iris recognition systems that can perform well in non-ideal scenarios.

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CNN‐based off‐angle iris segmentation and recognition

Cited by 10 publications

References 48 publications

An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

An iris segmentation using harmony search algorithm and fast circle fitting with blob detection

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

Off-angle iris recognition using single and multiple deep learning frameworks

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