Finger vein living body detection method based on multi-feature fusion and DE-ELM

Abstract

The invention relates to a finger vein living body detection method based on multi-feature fusion and DE-ELM, and the method comprises the following steps: 1) respectively collecting a true finger vein image and a false finger pseudo vein image as positive and negative training samples, and carrying out the size normalization preprocessing and Gaussian filtering processing of the positive and negative training samples; 2) respectively extracting a plurality of LBP histogram features and multi-scale HOG features of the vein image, and fusing the LBP histogram features and the multi-scale HOG features into a total feature vector for expressing vein features; 3) setting an activation function of neurons of the hidden layer, determining the number of the neurons of the hidden layer by using adifferential evolution algorithm (DE), and constructing a DE-ELM classification model; 4) inputting the training data into a DE-ELM classification model for training; and 5) inputting the test image data into the trained DE-ELM classification model for detection and identification of living body data, and determining whether the test image data is a living body finger vein. According to the invention, the algorithm for finger vein living body detection by combining multi-feature fusion with the DE-ELM classifier has the advantages of fast detection speed, high detection precision, strong robustness and the like.

Description

technical field [0001] The invention belongs to the technical field of finger vein recognition and information security, and in particular relates to a finger vein living body detection method based on multi-feature fusion and DE-ELM. Background technique [0002] In the field of finger vein biopsy detection, algorithms based on biopsy signal detection distinguish true and false veins by detecting finger activity or life signals. This type of algorithm has high accuracy and reliability, but often requires additional equipment or consumes more computing resources. Algorithms based on texture analysis take advantage of the differences in imaging quality between real and fake vein images, which are mainly reflected in texture and noise levels. This type of algorithm does not need to add additional equipment, nor does it require interactive actions that will reduce user experience, and can minimize the consumption of additional computing resources. At present, most researches ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a finger vein living body detection method based on multi-feature fusion and DE-ELM in order to solve the defects of slow learning speed of living body detection and poor performance of single feature image details in the prior art

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