Heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples

Abstract

Current iris recognition technology faces practical difficulties. For example, the unsteady morphology of a heterogeneous iris is generated by a variety of different devices and environments. The existing iris data sets have difficulty meeting the requirements of learning methods under a single deep learning framework. Research on iris recognition security has limited response to results stealing. Therefore, we propose a one-to-one heterogeneous iris certification method with universal sensors and safety output based on multisource fusion features and entropy labels of lightweight samples. This method is based on the classic convolutional neural network structure, and a lightweight neural network certification structure is designed. At first, convert the constrained multistate iris image into the digital features based on statistical learning and multisource feature fusion mechanism. The information entropy of the iris feature label is used to set the iris entropy feature category label and design a certification function that meets the requirements of different acquisition sensors according to the category label to obtain the certification result. Through the result encryption output module, the security output is achieved between the user and the certification result, and measures can be taken in time to confirm the stealing attack to improve the security of the output of certification result. As the requirement for the number and quality of irises changes, the category labels in the certification function are dynamically adjusted using a feedback learning mechanism. Three different acquisition sensors in the JLU iris library are used to do the experiments. The results prove that, for lightweight constrained multistate irises, the above mentioned problems are ameliorated to a certain extent by this method.