False fingerprint detection algorithm based on curvelet texture analysis and SVM-KNN classification

Abstract

The invention discloses a false fingerprint detection algorithm based on curvelet texture analysis and SVM-KNN classification. The false fingerprint detection algorithm based on the curvelet texture analysis and the SVM-KNN classification comprises the following steps of curvelet transformation; curvelet reconstruction; curvelet coefficient feature extraction, extraction of coefficient energy, entropy and the like, and coefficient first-order statistical magnitude extraction; textural feature extraction, and extraction of a first-order statistical magnitude, a gray-level co-occurrence matrix, MRF features and the like; training of classifiers; performance evaluation of the classifiers; false fingerprint detection, wherein testing is carried out on samples by utilizing the trained classifiers. The false fingerprint detection algorithm based on the curvelet texture analysis and the SVM-KNN classification is suitable for various fingerprint acquisition instruments of 500 dpi resolution, analysis is respectively carried out on image high-frequency noise information and texture information after denoising, and the noise and texture differences between true fingerprints and false fingerprints are quantified.

Description

technical field [0001] The invention relates to image processing, pattern recognition and other related technical fields, in particular to a false fingerprint detection algorithm based on curvelet texture analysis and SVM-KNN classification, which aims to detect fingerprints to distinguish true from false. Background technique [0002] Fake fingerprints generally refer to fingerprints that imitate the texture of human fingerprints made of materials such as silica gel, latex, and plasticine. The main steps of the algorithm include image processing, feature extraction, classifier training, and image classification. [0003] With the development of economy and society, biometric identification technology, especially fingerprint technology has been more and more widely used. However, the emergence of fake fingerprints made of some cheap materials and the improvement of their production technology have brought security risks for fingerprint feature recognition. Some studies have...

AI Technical Summary

Problems solved by technology

However, the LBP operator has the limitation of a small space support area, and it is not advisable to perform simple texture analysis, and it is not advisable to abandon the analysis of the noise difference of the acquired image caused by the different materials of the true and false fingerprints.

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