CNN model, CNN training method and vein identification method based on CNN

Abstract

The invention discloses a CNN model, a CNN training method and a vein identification method based on CNN. The CNN model comprises multiple convolution layers, a full connection stair layer and a SoftMax layer. In a CNN training process, a database is firstly expanded and multiple biological feature databases including similar features are combined to carry out a training of a model; the full connection layer and the SoftMax layer serve as a multi-classification classifier together; a multi-classification neural network is trained so that the neural network is allowed to learn features capable of identifying vein features; and after the training is finished, the previous layer of the full connection layer is output to serve as feature, and similarity of a pair of images is measured by calculating cosine distance of the features. According to the invention, multi-mode biological feature data is fused and used for training a network, so a problem of insufficient training samples is solved and retrieval speed can be greatly improved in a super large identity identification database.

Description

technical field [0001] The invention relates to the technical field of deep learning and vein recognition, in particular to a CNN model, a CNN training method and a CNN-based vein recognition method. Background technique [0002] With the development of science and technology, biometric identification technology has gradually replaced traditional identity authentication methods in some aspects, making people's lives more and more convenient, especially the finger vein identification technology has attracted more and more attention from researchers. Because veins are located under the skin, unlike fingerprints, they are not easily damaged by external factors, and because they are invisible to the naked eye, they are difficult to be stolen and forged, so they have extremely high stability and security. In addition, the device of finger veins is light and convenient, which makes finger vein recognition technology have great application prospects. However, there are still many p...

AI Technical Summary

Problems solved by technology

However, due to the problem of imaging equipment, vein images are generally blurred. This type of method will encounter problems similar to those based on minutiae features: the features extracted from blurred images are not stable enough.

However, due to the lack of databases available in the field of finger vein recognition, the application of deep learning in this field is limited.

Some people first tried some deep learning algorithms in the field of finger veins, but because the database is too small, its performance is not very ideal

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