A Code-Level Approach to Heterogeneous Iris Recognition

Liu, Nianfeng; Liu, Jing; Sun, Zhenan; Tan, Tieniu

doi:10.1109/tifs.2017.2686013

Cited by 34 publications

(9 citation statements)

References 27 publications

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“…Encoded binary strings have been considered as final feature for matching and SVM is used as a matching technique. Nianfeng Liu et al, [9] proposed A Code-level Approach in heterogeneous iris recognition. Method adapted Markov network to model a non-linear relationship between binary feature codes of heterogeneous iris images.…”

Section: Literature Surveymentioning

confidence: 99%

Multi Scale ICA Based IRIS Recognition Using BSIF and HOG

Sagar¹,

Nc²,

K³

et al. 2017

SIPIJ

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Section: Literature Surveymentioning

confidence: 99%

Multi Scale ICA Based IRIS Recognition Using BSIF and HOG

Sagar¹,

Nc²,

K³

et al. 2017

SIPIJ

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“…In addition, for label correction of the existing neural network architecture, an error correction code-based label optimization method [17] and a feedback mechanism-based label correction method [18] have been proposed. 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].…”

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

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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.

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“…In addition, the acquired image can be processed by an algorithm to accurately locate the iris position, extract the iris effective information, and suppress the interference caused by the change of the texture state, including, in addition, the possibility of processing the acquired image using some algorithms to suppress the interference caused by the changes of texture state. The main research direction is heterogeneous iris recognition [5], including fuzzy image correction [6], effective feature region rotation adjustment [7], unconstrained iris localization [8], iris region selection of interest [9], and effective information extraction based on seeded model [10]. This method has high operability and low requirements on the acquisition equipment.…”

Section: Introductionmentioning

confidence: 99%

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Liu

Zhu

et al. 2019

Algorithms

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Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.

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A Code-Level Approach to Heterogeneous Iris Recognition

Cited by 34 publications

References 27 publications

Multi Scale ICA Based IRIS Recognition Using BSIF and HOG

Multi Scale ICA Based IRIS Recognition Using BSIF and HOG

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

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

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