Iris image feature extraction method, system and device based on federated learning

Abstract

The invention belongs to the field of image recognition, and specifically relates to a federated learning-based iris image feature extraction method, system and device, aiming to solve the problem that the existing iris feature extraction model cannot take into account privacy protection and multi-party cooperation, and simply stacks iris data The problem that model training cannot improve the recognition performance of the model. The present invention obtains the local iris features of the local iris data set, and then generates local feature triples and local triple losses, and calculates the Wasserstein federation triple loss function in a third party to update the local platform according to the Wasserstein federation triple loss function The iris image feature extraction network and the new iris image feature extraction network are used to extract new iris image features. The present invention utilizes the distribution characteristics and rules of iris features from different sources in the feature space to assist self-model training, so that the extracted features are better distinguishable, thereby improving the iris feature expression ability and recognition accuracy of each partner .

Description

technical field [0001] The invention belongs to image recognition, and in particular relates to a federated learning-based iris image feature extraction method, system and device. Background technique [0002] As a biometric feature with high accuracy and good security, the iris is being widely used in various identification and security control scenarios. After strict, it is not feasible to build a large-scale iris data center for stack training. Therefore, it is imperative to establish a federated iris recognition model training and feature learning framework based on privacy protection and multi-party cooperation. Federated learning can combine multi-party data for training, which is more suitable for occasions with high security and privacy requirements. On the other hand, the federated iris recognition model training and feature learning framework can combine multiple iris databases and various iris recognition neural networks for collaborative learning, fully balance ...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, by collecting multiple iris data sets, without considering data security and privacy protection, a single data center is established, all data are directly stacked and mixed together, and sent to models such as deep neural networks for feature learning training Way

[0004] The iris recognition model based on the deep learning framework usually requires a large number of iris samples for learning, but the construction of a large-scale iris data center has security risks and privacy protection limitations, because the original biometric data, which is highly private, will be irreversible once leaked. However, the generalization ability and marginal effect of the iris recognition model lacking a large amount of training data will be greatly reduced in practical applications. Therefore, it is particularly important to establish a framework for joint multi-party data decentralized iris recognition model training and feature learning.

In addition, most of the current iris data sets have obvious differences in collection rules, collection equipment, collection environment, data scale, image quality, etc., and the distribution of different iris databases is significantly different, simply relying on stacking unlimited irises The data cannot continuously improve the recognition performance of the model, and may even have a counterproductive effect

At present, there is a lack of an iris recognition model training and feature learning framework that takes into account privacy protection and multi-party cooperation to fully tap the potential and deep value of multi-party data, so that the recognition models of each partner can benefit from the spatial distribution of other data sets and model feature expression ability, so as to further improve its recognition ability and generalization ability

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