Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Karakaya, Mahmut

doi:10.1117/1.jei.30.3.033035

Cited by 13 publications

(4 citation statements)

References 46 publications

(57 reference statements)

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“…This research paper has explored the possibilities of applying deep learning on classifying retinal diseases. The main concepts behind CNNs have been explained, along with the importance of proper hyperparameter tuning 28 . Multiple architectures have been tested to find the optimal solution.…”

Section: Discussionmentioning

confidence: 99%

“…The main concepts behind CNNs have been explained, along with the importance of proper hyperparameter tuning. 28 Multiple architectures have been tested to find the optimal solution. The analysis of different architectures that can be applied in the classification of retinal diseases based on OCT images as well as the evaluation of the test set obtained by comparing 64 models with different hyperparameters represents the main contributions of this research.…”

Section: Table 12mentioning

confidence: 99%

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Retinal disease classification based on optical coherence tomography images using convolutional neural networks

2022

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The challenges in today's medicine are progressively more related to the application of artificial intelligence and supervised learning techniques. Optical coherence tomography (OCT) is a noninvasive imaging technology used to obtain high-resolution cross-sectional images of the retina. The layers within the retina can be differentiated and retinal thickness can be measured to facilitate early detection and diagnosis of retinal diseases and conditions. This research paper is aimed at exploring different possibilities of applying deep learning, specifically convolutional neural networks in retinal diseases. During the research, several different architectures-AlexNet, VGG, Inception, and residual network, were evaluated, and the convolutional neural network that proved to be the most successful in the classification was based on the Inception architecture. Hyperparameter tuning was applied as the main method to find the most optimal solution. The key contributions of this research refer to the analysis of different architectures that can be applied in the classification of retinal diseases based on OCT images, as well as the evaluation of the test set obtained by comparing different models with different hyperparameters. This research yielded the best results obtained with Inception1, when training by means of the root mean square propagation optimizer with a batch size of 32, learning rate of 1e −6 , momentum of 0.99, and L2 regularization rate of 0.001. This model achieved an accuracy of 0.95528. In the conclusion of the paper, the advantages of the proposed and implemented solutions were discussed, and a proposal for further improvements was proposed.

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Section: Discussionmentioning

confidence: 99%

Section: Table 12mentioning

confidence: 99%

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

2022

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“…This study builds upon our previous work in [9], which tackled the issue of performance degradation in off-angle images resulting from differences in gaze angle. In our previous study, we presented deep learning frameworks based on convolutional neural networks (CNNs) to enhance the recognition accuracy of off-angle iris, ocular, and periocular biometric modalities.…”

Section: Related Workmentioning

confidence: 98%

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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“…Biometrics have proved to be the preferred way of establishing the identity of individuals in security applications ranging from law enforcement and surveillance to smartphone access control and attendance management. Biometric systems typically make use of physiological traits (face, 1 fingerprints, 2 , 3 iris, 4 , 5 etc.) or behavioral properties (keystroke dynamics, 6 gait, 7 etc.)…”

Section: Introductionmentioning

confidence: 99%

Dual-spectrum network: exploring deep visual feature to attribute mapping for cross-spectral periocular recognition

Behera

Puhan

2023

J. Electron. Imag.

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.Recognition of individuals using periocular images has become popular due to its ease of use in situations in which it is difficult to capture full-face regions or high-resolution iris images. Practical surveillance systems recognize individuals from images acquired in various imaging conditions. Recognition of individuals in cross-spectral environments is of practical interest as it can be used to match probe and gallery images that are captured in distinct wavelength domains. The process of matching probe near-infrared images with a gallery of visible images is significantly challenging due to variations in appearance and illumination conditions. In periocular regions, alleviating this wide appearance gap from such local regions is highly challenging. In this work, we propose a dual-spectrum network that considers both high-level deep features and low-level handcrafted attributes to match periocular images in cross-spectral scenarios. A mapping network is designed to establish the relationship between both types of features and generate mapped features that carry discriminative information relevant to both feature domains. The mapped features are passed through a distance layer and subsequent classification block to determine whether they are a genuine pair or an imposter pair. The network is evaluated on a publicly available PolyU dataset as well as two newly created in-house CASIA and IITBBS cross-spectral periocular datasets. The IITBBS dataset consists of visible and near-infrared periocular images from 100 subjects captured in unconstrained imaging conditions with pose and accessory variations. Extensive experiments indicate that the proposed network achieves verification accuracies of 97.96%, 96.48%, and 97.89% on the PolyU, CASIA, and IITBBS datasets, respectively. Performance analysis with existing methods confirms that our network achieves comparable results on the PolyU and CASIA datasets. In addition, the network outperforms existing works on the challenging IITBBS dataset by achieving improvements of 0.43% and 0.16% in terms of verification accuracy and genuine acceptance rate at a 0.1 false acceptance rate.

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Iris-ocular-periocular: toward more accurate biometrics for off-angle images

Cited by 13 publications

References 46 publications

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

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

Dual-spectrum network: exploring deep visual feature to attribute mapping for cross-spectral periocular recognition

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