Small data set finger vein recognition method based on neural network

Abstract

The invention discloses a small data set finger vein recognition method based on a neural network. The method comprises the following steps: constructing and preprocessing a data set; carrying out data expansion on all types of images by utilizing a data enhancement algorithm; utilizing a convolutional neural network to construct a model suitable for data; training the designed model; acquiring atarget image by using acquisition equipment for preprocessing, and outputting a finger vein feature vector of the preprocessed image through model output; and comparing with a stored finger vein feature vector template to determine the category of the input finger vein. According to the method, through combination of a neural network and data enhancement, features with robustness are extracted; and through Euclidean distance comparison between different image output feature vectors, different types of finger vein images are distinguished according to the size of the distance, so that finger vein recognition can obtain high accuracy on the premise of a small data set.

Description

technical field [0001] The invention relates to the technical field of image pattern recognition, in particular to a neural network-based small data set finger vein recognition method. Background technique [0002] Finger vein recognition is a biometric technology for identification based on human finger vein feature information. Compared with biotechnology such as face and fingerprint, its significant advantage is that the characteristics of finger vein recognition come from the hemoglobin in the blood flowing in the finger vein, which is secretive and alive; the collection process is non-contact, which ensures the safety of the collection process health. Due to the increasing demand for security and anti-counterfeiting in public security, identity authentication, digital entertainment industry and other fields, finger vein recognition technology has attracted more and more attention from academia and industry. The application scenarios of finger vein recognition are very...

AI Technical Summary

Problems solved by technology

By acquiring a large amount of image data, training a deep neural network model, and then performing recognition, the accuracy rate is high and the robustness is strong, but it requires massive data, long training time, and powerful hardware conditions, which cannot be met in specific application scenarios.

The above limitations make the current finger vein recognition method based on traditional image processing feature extraction less robust, and it is difficult to use deep learning to recognize finger veins

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