Heterogeneous Iris One-to-One Certification with Universal Sensors based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

Liu Shuai; Liu Yuanning; Zhu Xiaodong; Huo Guang; Wu Zukang; Li Xinlong; Wang Chaoqun; Cui Jingwei

doi:10.3390/s20061785

Heterogeneous Iris One-to-One Certification with Universal Sensors based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

Sensors (Basel). 2020 Mar 23;20(6):1785. doi: 10.3390/s20061785.

Authors

Liu Shuai^{1

2}, Liu Yuanning^{1

2}, Zhu Xiaodong^{1

2}, Huo Guang³, Wu Zukang^{1

2}, Li Xinlong^{2

4}, Wang Chaoqun^{2

4}, Cui Jingwei^{2

4}

Affiliations

¹ College of Computer Science and Technology, Jilin University, Changchun 130012, China.
² Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
³ College of Computer Science, Northeast Electric Power University, Jilin 132012, China.
⁴ College of Software, Jilin University, Changchun 130012, China.

Abstract

Due to the unsteady morphology of heterogeneous irises 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. Traditional iris recognition divides the whole process into several statistically guided steps, which cannot solve the problem of correlation between various links. 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, aiming at a one-to-one iris certification scenario, this paper proposes a heterogeneous iris one-to-one certification method with universal sensors based on quality fuzzy inference and a multi-feature entropy fusion lightweight neural network. The method is divided into an evaluation module and a certification module. The evaluation module can be used by different devices to design a quality fuzzy concept inference system and an iris quality knowledge concept construction mechanism, transform human logical cognition concepts into digital concepts, and select appropriate concepts to determine iris quality according to different iris quality requirements and get a recognizable iris. The certification module is a lightweight neural network based on statistical learning ideas and a multi-source feature fusion mechanism. The information entropy of the iris feature label was used to set the iris entropy feature category label and design certification module functions according to the category label to obtain the certification module result. As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different sensors in the JLU(Jilin University) iris library. The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

Keywords: feedback learning mechanism; heterogeneous iris one-to-one certification; iris quality knowledge concept construction mechanism; lightweight neural network; multi-source feature fusion mechanism; quality fuzzy inference; universal sensors.

Abstract

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