Multi-spectral Imaging for Robust Ocular Biometrics

Vetrekar, Narayan; Raja, Kiran B.; Ramachandra, Raghavendra; Gad, Rajendra S.; Busch, Christoph

doi:10.1109/icb2018.2018.00038

Cited by 12 publications

(9 citation statements)

References 16 publications

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“…This resulted in an effective rate of return of 20.07 percent. In compared to a prior three-layer neural network baseline approach, the PER obtained is better [66]. Smartphones and their camera resolution have been tried and tested for iris recognition.…”

Section: Biometricsmentioning

confidence: 99%

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Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Taye

2023

Computers

202

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In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial neural networks (ANN), has become a big deal in computing because it can learn from data. The ability to learn enormous volumes of data is one of the benefits of deep learning. In the past few years, the field of deep learning has grown quickly, and it has been used successfully in a wide range of traditional fields. In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches. In order to provide a more ideal starting point from which to create a comprehensive understanding of deep learning, also, this article aims to provide a more detailed overview of the most significant facets of deep learning, including the most current developments in the field. Moreover, this paper discusses the significance of deep learning and the various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be utilised. We conclude by identifying possible characteristics for future generations of deep learning modelling and providing research suggestions. On the same hand, this article intends to provide a comprehensive overview of deep learning modelling that can serve as a resource for academics and industry people alike. Lastly, we provide additional issues and recommended solutions to assist researchers in comprehending the existing research gaps. Various approaches, deep learning architectures, strategies, and applications are discussed in this work.

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Section: Biometricsmentioning

confidence: 99%

“…Deep learning algorithms improve as they get more experience. The next few years should be remarkable as technology continues to advance [66].…”

Section: Biometricsmentioning

confidence: 99%

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Taye

2023

Computers

202

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“…The CASIA dataset 36 contains VIS and NIR periocular images from 200 subjects, cropped originally from the existing heterogeneous CASIA NIR-VIS 2.0 face dataset. 70 Vetrekar et al 19 used an automatic face landmark detection method to locate eye coordinates and crop periocular regions. A similar approach is employed in the case of the CASIA dataset in which the Viola-Jones algorithm 71 is used to crop periocular regions automatically from the face images.…”

Section: Datasets and Experiments Protocolmentioning

confidence: 99%

“… – 15 Instead of using simple distance measures, some works focused on training additional classifiers using handcrafted features to classify periocular images in heterogeneous domains. Such classifiers included neural networks, 16 Markov random fields, 17 support vector machines, 18 and collaborative representation classifiers 19 . Sequeira et al 20 , 21 .…”

Section: Related Workmentioning

confidence: 99%

“…Such classifiers included neural networks, 16 Markov random fields, 17 support vector machines, 18 and collaborative representation classifiers. 19 Sequeira et al 20,21 discussed cross-spectral iris/periocular recognition challenges and evaluated a number of methods on the heterogeneous cross-eyed dataset 20 using a generalized evaluation metric.…”

Section: Introductionmentioning

confidence: 99%

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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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Multimodal Multilevel Fusion of Face Ear Iris with Multiple Classifications

Purohit¹,

Ajmera²

2020

Lecture Notes in Electrical Engineering

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Multi-spectral Imaging for Robust Ocular Biometrics

Cited by 12 publications

References 16 publications

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

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

Multimodal Multilevel Fusion of Face Ear Iris with Multiple Classifications

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