Fake fingerprint detection method based on markov random field (MRF) and support vector machine-k nearest neighbor (SVM-KNN) classification

Abstract

A fake fingerprint detection method based on markov random field (MRF) and support vector machine-k nearest neighbor (SVM-KNN) classification includes steps: (1) fingerprint image feature extracting: (1.1) first-order statistics (FOS), (1.2) a gray level co occurrence matrix (GLCM) and (1.3) an MRF; (2) SVM training: training the FOS and the GLCM feature vector and the MRF feature vector to obtain a model A and a model B; (3) SVM-KNN classification: (3.1) the SVM classification mechanism and (3.2) SVM-KNN classifier forming; and (4) decision fusion for true and false fingerprint detection. Presently related articles for fake fingerprint detection by aid of the GLCM and the MRF are not found, and the fake fingerprint detection method achieves the purpose of identifying true and false fingerprints by aid of physical structures of the two feature quantized fingerprint images. Experiment results prove that the false accept rate and the false reject rate of the algorithm are respectively 1.84% and 1.79%, and therefore the fake fingerprint detection method is high in accuracy and good in practicality.

Description

technical field [0001] The invention relates to the technical fields of image processing, pattern recognition and the like, and the main content is a method for detecting false fingerprints. Background technique [0002] Image feature extraction, feature training, and decision fusion are important knowledge points in the field of image processing and pattern recognition, and they are all closely related to the effect of fake fingerprint detection methods. The process of fake fingerprint detection method is mainly divided into four steps: image feature extraction, SVM training, SVM-KNN classification and decision fusion, among which feature extraction is particularly important in the process of fake fingerprint detection. [0003] Emanuela Marasco proposes to extract the texture feature information of fingerprint images, and detect fake fingerprints through first-order statistics. When the resolution of the acquisition instrument is high, this method has a better false finge...

AI Technical Summary

Problems solved by technology

However, the SVM classification algorithm has a disadvantage: when the sample distance from the classification hyperplane is less than a given threshold ε, its classification accuracy will decrease.

[0010] At present, the commonly used fake fingerprint detection methods can be divided into two categories: the first category uses characteristics such as finger temperature, skin conductivity, pulse oximetry, etc. These characteristics can be detected by adding additional hardware devices to the fingerprint collector, but Will increase the cost of the collector, this type of method is called a hardware-based fake fingerprint detection method

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