Identity authentication method and system based on electroencephalogram characteristics

Abstract

The invention discloses an identity authentication method and system based on electroencephalogram characteristics. The method comprises the following steps: model training, password setting, alpha wave test, electroencephalogram characteristic extraction and classification, and model updating. The electroencephalogram characteristic extraction and classification method is composed of pre-training layer by layer, network fine tuning and classification. According to the identity authentication method and system disclosed by the invention, first level authentication is performed on a tester by means of the property that the alpha waves fluctuate greatly when the tester is lying, meanwhile the existing method is improved to establish a feature extraction method based on a depth belief network, meanwhile the feature extraction method is optimized by using an immune leapfrog algorithm, and when the tester observes password patterns, feature extraction and classification are performed on the brain waves so as to perform second level authentication. By means of the two levels of authentication, the security of the user can be guaranteed, and the improved depth belief network can also improve the existing identification precision, ensure less error rates, avoid a large number of false alarms and bring good user experience.

Description

technical field [0001] The present invention relates to the field of Internet of Things technology and information security technology, in particular to an identity authentication method and authentication system based on EEG features. Background technique [0002] Traditional identity authentication methods include voice authentication, face recognition, password authentication, fingerprint authentication, and iris authentication. These traditional identity authentication methods have many disadvantages: some of them are easy to be breached, such as password leaks, fingerprints stolen, etc.; Therefore, an identity authentication system that is difficult to break, difficult to leak, and has high recognition accuracy came into being-the brain wave identity authentication system. Experimental studies have shown that EEG characteristics are unique to each person. Even when the same person is facing different events or in different mental states, EEG characteristics will be ver...

AI Technical Summary

Problems solved by technology

There are many shortcomings in these traditional identity authentication methods: some of them are easy to be broken, such as password leakage, fingerprints stolen, etc.;

[0003] Most of the existing brainwave identity authentication systems use convolutional neural networks or deep belief networks as feature extraction and classification methods. However, the implementation of convolutional neural networks is relatively complicated, and the cost of specific instantiation is high, so it is not suitable for promotion. , and due to the separate training of different tasks, the training time is relatively long, so users may not be able to use it in time; while the traditional deep belief network sometimes falls into local optimum due to the random assignment of weights between layers and produces "premature " phenomenon, so the present invention proposes to use the deep belief network optimized by the immune leapfrog algorithm to extract and classify brain wave features, so that not only can jump out of the local optimal solution, but also speed up the calculation

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