Method for constructing finger vein recognition system and finger vein recognition system

Abstract

The embodiment of the invention provides a method for constructing a finger vein recognition system and the finger vein recognition system, and the method comprises the steps: A1, constructing an initial finger vein recognition system which comprises a preprocessing module, a feature extraction module employing a non-finger vein data set for pre-training, and a domain algorithm module; a2, obtaining finger vein grey-scale maps corresponding to a plurality of fingers, and training a preprocessing module to preprocess the finger vein grey-scale maps so as to extract finger vein vein maps of thefingers; a3, training the feature extraction module to extract a finger vein feature vector of the finger according to the finger vein map transfer learning, wherein training data in a triple form generated based on the finger vein map is adopted as the training data; and a4, training the domain algorithm module to establish different recognition areas for different fingers according to the fingervein feature vectors, and analyzing the similarity between the target finger and the finger corresponding to each recognition area according to the finger vein feature vectors.

Description

technical field [0001] The present invention relates to the field of deep learning, specifically to the field of biological feature recognition using a neural network, and more specifically to a method for constructing a finger vein recognition system and a finger vein recognition system. Background technique [0002] The finger vein recognition technology confirms the identity of the person through the recognition of the vein vein pattern of the finger. The oxygen-deficient hemoglobin in the veins can absorb near-infrared rays. When the near-infrared rays irradiate the finger, a small camera with near-infrared sensitivity can be photographed at the finger to obtain an image of the vein pattern. [0003] At present, finger vein technology is widely used in identity authentication devices in the public domain, such as: bank ATM machines, access control management systems, computer logins, car locks, safe locks, or electronic payments, etc. that require personal identity authe...

AI Technical Summary

Problems solved by technology

Therefore, it is theoretically possible to train the neural network through finger vein images to obtain better recognition results. However, the number of samples in the currently available finger vein image datasets is not large enough, resulting in direct training through the existing finger vein image datasets. The recognition accuracy of the obtained finger vein recognition system is not high

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