Harris corner detection image pyramid palm print ROI (region of interest) recognition method

Abstract

The present invention belongs to the digital image processing technical field and discloses a Harris corner detection image pyramid palm print ROI (region of interest) recognition method. The method includes the following steps that: an entered palm print ROI image and a template palm print ROI image are inputted, MFRAT filtering is performed on the two images; the Harris corner detection algorithm is used to extract corners distributed on palm print main lines and wrinkles from the filtered template palm print ROI image with only one corner reserved in each small block region; the image pyramid algorithm is adopted to obtain corners distributed on the filtered entered palm print image according to the corners on the filtered template palm print ROI image; and the average BLPOC (Band-Limited Phase-Only Correlation) algorithm is adopted to obtain a final matching score according to known corner distribution; and a fixed threshold value is set according to an experiment, if the matching score is larger than the threshold value, a palm print is a true match, otherwise, the palm print is a false match. With the Harris corner detection pyramid palm print ROI (region of interest) recognition method of the invention adopted, palm print image ROI matching can be performed accurately.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to a Harris corner detection image pyramid palmprint ROI recognition method. Background technique [0002] Biometric technology can well solve the shortcomings of traditional identification technology. Palmprint recognition technology stands out among biometric recognition technologies due to its advantages of easy collection, low equipment cost, abundant available information, and good matching performance, and has a very broad application prospect. At present, texture-based palmprint recognition and matching methods have good matching performance; palmprints contain rich texture information, such as palmprint main lines, folds, forks, and so on. The main idea of ​​the texture-based palmprint recognition method is to encode the direction of the intercepted palmprint ROI image to extract the most significant feature of each pixel. During the process of palmprint c...

AI Technical Summary

Problems solved by technology

[0003] To sum up, the problems existing in the prior art are: traditional palmprint ROI image matching is prone to error matching, and the algorithm efficiency is low

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