Current optimal active feedback and stealing response mechanism for low-end device constrained defocused iris certification

Addressing the problem of iris certification security and unclear textures caused by defocused scanning, the constrained defocused state iris scanned by a low-end device is taken as the research object. A codeless mode certification algorithm for label statistical learning based on current optimal active feedback is proposed. The certification model is improved based on the convolutional neural network structure. Guided by statistical learning, the features from the same category irises are set to the feature label of one category, and the iris certification function is designed. In addition, a current optimal active feedback mechanism is proposed. The certification effects of the certification model will be actively reflected in real time based on the current training data and the accumulated certification data, and the user can adjust the model structure and parameters in real time, to maintain the current optimality of the certification model structure. In addition, a result mapping layer included a stealing response mechanism is set between the certification result and the user. The stealing behavior that occurs can be made meaningless by changing the result output mapping relationship. Under the algorithm prerequisites, the JLU iris library is used to conduct experiments, and the accuracy of the algorithm can reach more than 99%, which indicates that this algorithm can effectively improve the accuracy of constrained state defocused iris scans with a low-end device, the model can be adjusted dynamically, and stealing attack behavior can be responded to effectively.