Statistical Cognitive Learning and Security Output Protocol for Multi-State Iris Recognition

Abstract

In this paper, the research focuses on the constrained iris under different acquisition states, and trains the mixed data without manual label division. A multi-state iris multi-classification recognition method based on convolutional neural network fusion statistical cognitive learning is proposed. The recognition process is divided into an image processing module, a classification module and a result output module. The image processing module converts iris images into recognition tags through convolutional neural network. By combining the characteristics of cognitive learning and statistical learning, the classification module converts iris features of the same category to the convolutional neural network label parameters by data statistical, and forms a single iris cognitive concept, thereby designing a single-category recognizer. Multiple single recognizers are combined in parallel to perform multi-classification recognition by the codeless fusion recognition mode, the results are further encrypted by external encryption. The final classification result is exported through the result output module, which can improve the security of data transmission. After confirming stealing attacks, the result output security protocol is activated, and legitimate users who pass the reliable third-party authentication can still get correct results after changing the decryption key and the result output mapping path. In the multi-state iris recognition, the experimental results of JLU iris library demonstrate that the proposed method can ensure the correct rate of the single classifier certification. In multi-classification recognition, it can effectively cluster iris of the same category and distinguish different categories of irises. In addition, the method can also effectively respond to the stealing attack behavior.