Finger vein biological key generation method based on deep neural network coding

Abstract

The invention discloses a finger vein biological key generation method based on deep neural network coding. By combining a classical finger vein image processing method and a deep neural network processing method of a finger vein original image, compared with an existing classical finger vein feature point extraction method, stable feature components of different samples of the same finger vein can be extracted more accurately, and through layer-by-layer processing of the deep neural network, features are kept to be feature values. finally, through a finger vein fuzzy extractor, high-intensity key sequence generation of a normal finger vein image is realized, and the length of the generated finger vein biological key can be greater than 256 bits. Biological feature template information needing to be recorded does not exist, the risk of privacy disclosure is greatly reduced, meanwhile, a user can generate a high-safety secret key without high-intensity memory, the secret key can be used for existing public and private keys, symmetric encryption and other operations, and the safety and flexibility of finger vein biological feature use are improved.

Description

technical field [0001] The invention belongs to the technical field of information security, and in particular relates to a method for generating a biological key from human finger vein images via deep neural network encoding. The generated key can be used for identity authentication and encryption operation, which can be understood as a kind of ubiquitous encryption technology in network security. Background technique [0002] Compared with other biometric features, finger vein recognition has stronger universality and uniqueness. In vivo recognition, finger surface skin conditions do not affect the recognition work, non-contact collection and other advantages are gradually being valued in the field of local identity authentication. The framework of finger vein recognition is similar to that of the traditional biometric identification technology, both of which adopt the method of obtaining biometric information images first, and finger vein recognition generally uses infrar...

AI Technical Summary

Problems solved by technology

This method can have a certain stabilizing effect on the finger vein feature sequence, but it is limited by the sensitivity of the processing method to the difference in illumination, and the number of stable feature components that can be extracted is not large, so that the length of the stable bit sequence that can be extracted is insufficient (generally <256bit), the overall success rate and strength of key generation are not high enough

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