Finger vein verification method and device based on neural network and storage medium

Abstract

The invention discloses a finger vein verification method and device based on a neural network and a storage medium. Finger vein verification is carried out by adopting a dual-channel network with only three convolution layers; the problem of insufficient training samples in finger vein recognition is effectively solved. According to the method, firstly, an original image is acquired, then the sensitivity to finger displacement in the original image is solved by extracting the minimum region of interest from the original image, and finally, the original image and the minimum region of interestare integrated by using a double-flow network, so that the problem of insufficient training samples in finger vein recognition is effectively solved, and the requirement of effective solution of a neural network is met.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a neural network-based finger vein verification method, device and storage medium. Background technique [0002] As people have higher and higher requirements for identification, finger vein recognition, as an emerging biometric technology, has attracted widespread attention in the field of biometric authentication. Compared to other biometric technologies such as face, gait, and fingerprints, finger vein technology has some distinct advantages: higher user-friendliness, liveness detection, high security, and small device size, which makes it very Suitable for high security and user-friendly applications. In general, the verification process of finger veins includes the following four steps: image acquisition, preprocessing, feature extraction, and matching. Among these steps, feature extraction plays a key role. [0003] Deep features learned from deep convolutional n...

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Problems solved by technology

[0004] In order to solve the above problems, the object of the present invention is to provide a neural network-based finger vein verification method, device and storage medium, which effectively solves the problem of insufficient training samples in finger vein recognition and meets the needs of neural networks for effective solutions.

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