Optimized robust multi-sensor scheme for simultaneous video and image iris recognition

Llano, Eduardo Garea; Vázquez, Mireya Saraí García; Vargas, J. Mario; Fuentes, Luis Miguel Zamudio; Acosta, Alejandro

doi:10.1016/j.patrec.2017.11.012

Cited by 24 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As part of the proposed scheme, they presented a fusion method based on the modified Laplacian pyramid in the segmentation stage. The experimental results in the Casia-V3-Interval, Casia-V4-Mile, Ubiris-V1, and MBGC-V2 databases show that the robust optimized scheme increases recognition accuracy and is robust for different types of iris sensors [67].…”

Section: Literature Reviewmentioning

confidence: 96%

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Al-Ani

2019

UHD J SCI TECH

View full text Add to dashboard Cite

Biometric recognition is an emerging technology that has attracted more attention in recent years. This issue uses physiological and behavioral characteristics to identify an individual. The iris recognition approaches are among the most accurate biometric technologies with immense potential for use in global security applications. The purpose of this research is to implement an efficient approach to treat the diseased eye images in order to verify the second iris examination for the same person by inserting an improvement step to the iris recognition system. This step makes a correction of boundary around pupil and removes fraction regions. This approach demonstrates its ability to detect the inner limit of the iris. The obtained results shows that 90% success in the detection diseased eye images, which makes the iris recognition system more accurate and safe.

show abstract

Section: Literature Reviewmentioning

confidence: 96%

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Al-Ani

2019

UHD J SCI TECH

View full text Add to dashboard Cite

show abstract

“…The common reference image evaluation methods are the image histogram method, variance method, Tenengrad method, Laplacian method, fast Fourier transform method, grey variance method, histogram entropy method, and local histogram method [27]. In recent years, the Kang & Park method [28], X-ray tomography [29], StyleGAN [30] etc. are used to analyse and improve image sharpness.…”

Section: Image Quality Evaluationmentioning

confidence: 99%

Fast Blind Deblurring of QR Code Images Based on Adaptive Scale Control

et al. 2021

View full text Add to dashboard Cite

With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications.

show abstract

“…In the study of iris generality, heterogeneous iris recognition is the main direction [19]. First, by changing the internal structure of the algorithm, the device independence of the iris image is improved [20]. Second, correction of images (blurring, displacement, etc.)…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Feature Fusion and Entropy Feature Lightweight Neural Network for Constrained Multi-State Heterogeneous Iris Recognition

Liu

Zhu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Current iris recognition technology faces practical difficulties. For example, due to the unsteady morphology of a heterogeneous iris generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, this paper proposes a method of heterogeneous iris recognition based on an entropy feature lightweight neural network under multi-source feature fusion. The method is divided into an image-processing module and a recognition module. The image-processing module converts the iris image into a recognition label via a convolutional neural network. The recognition module is based on statistical learning ideas and design of a multi-source feature fusion mechanism. The information entropy of the iris feature label is used to set the iris entropy feature category label and design the recognition function according to the category label to obtain the recognition result. As the requirement for the number and quality of irises changes, the category labels in the recognition function are dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different devices in the JLU iris library. The experimental results prove that for multi-category classification of lightweight constrained multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

show abstract

Optimized robust multi-sensor scheme for simultaneous video and image iris recognition

Cited by 24 publications

References 6 publications

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Fast Blind Deblurring of QR Code Images Based on Adaptive Scale Control

Multi-Source Feature Fusion and Entropy Feature Lightweight Neural Network for Constrained Multi-State Heterogeneous Iris Recognition

Contact Info

Product

Resources

About