Method and apparatus for extraction and matching of biometric detail

Abstract

A method and apparatus of identification by extracting and matching biometric detail from a subcutaneous vein infrared image. The image's Region of Interest is identified and artifacts are removed. A bank of filters, such as Symmetric Gabor Filters, Complex Gabor Filters, Log Gabor Filters, Oriented Gaussian Functions, or Wavelets, filters the image into a set of key value images that are subdivided into regions. An enrollment key, defined by ordered statistical measures of pixel intensities within the regions, is compared using a distance metric to a stored verification key. Various statistical measures may be used, such as variance, standard deviation, mean, absolute average deviation, max value, min value, max absolute value, median value, or a combination of these statistical measures. Various distance metrics may be used, such as Euclidean, Hamming, Euclidean Squared, Manhattan, Pearson Correlation, Pearson Squared Correlation, Chebychev, or Spearman Rank Correlation.

Description

technical field [0015] The present invention relates generally to identifying individuals using biometric information, and more particularly to identifying and authenticating individuals using images of subcutaneous veins. From a technical point of view, the present invention proposes a method for identifying an individual by extracting and matching biometric details from an infrared image of a person's subcutaneous veins. Background technique [0020] Biometrics refer to identification or identification based on physical or behavioral characteristics. Biometrics are increasingly being used to provide positive identification with a high degree of confidence, often through 1:1 (one-to-one) identification or 1:n ( One-to-many) matching / identification to identify and / or authenticate an individual using biometric information. It should be understood that, as used herein, the term "identify" refers to both authentication (confirming that a person is who they claim to be) and ide...

AI Technical Summary

Problems solved by technology

Due to sensor noise and other adverse factors, there is no guarantee that the same set of points will be extracted each time an individual is authenticated / identified

Thus, such prior art methods must be flexible and take into account missing and extra point locations, which prevents them from constructing fixed-length keys that are always ordered in a uniform manner

As a result, the matching process is severely complicated and it becomes difficult to quickly search the vast database in the manner taught by the prior art

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