Finger vein identifying method based on characteristic value normalization and bidirectional weighting

Abstract

The invention provides a finger vein identifying method based on characteristic value normalization and bidirectional weighting. The method comprises the following steps of: (1) collecting finger vein images by an image collecting device; (2) carrying out preprocessing on the collected finger vein images, wherein the preprocessing comprises the steps of graying the color images, extracting finger areas, respectively eliminating salt and pepper noise and Gauss noise by adopting a combined filter, segmenting and binarizing the images by adopting a local dynamic threshold algorithm, de-noising by adopting an area eliminating method, extracting the finger vein venation images by finger contour markers, and standardizing the sizes of the images into that of the uniform images; (3) extracting a finger vein characteristic by a two-dimensional main component analyzing algorithm with weighting in two directions of the line; and (4) matching and identifying by a most adjacent classifier. The invention is used for a finger vein identifying system, obviously enhances the identifying speed of the finger veins, and has stable and high identifying rate.

Description

technical field [0001] The invention relates to a biological feature identification technology, which is a finger vein feature identification technology. Background technique [0002] Biometric Identification Technology (Biometric Identification Technology) refers to a technology that uses human biometrics for identity authentication. Unlike traditional methods, biometric methods are based on the individual characteristics that our bodies possess. Biometrics are divided into two categories based on physical characteristics and behavioral characteristics. Physical characteristics include: fingerprints, palm shape, eyes (retina and iris retina), human body odor, face shape, skin pores, hand blood vessel texture and DNA, etc. Behavioral characteristics include: signature, voice, walking gait, keystroke strength Wait. [0003] At present, most of the more mature biometric identification systems are based on single-mode, and there are still some problems in practical applicati...

AI Technical Summary

Problems solved by technology

[0005] (1) Due to dirt on the finger, too wet, too dry, or the fingerprint device is not clean, or the camera for collecting fingerprints is not focused properly, etc., the captured fingerprint image is not clear enough, causing image quality problems and directly affecting fingerprint recognition precision and results;

[0006] (2) According to the NIST (National Institute of Standards and Technology) report, due to finger injuries (scars, abrasions), or about 2% of finger skin bursts, can not provide good quality images to be tested and registered, so these People cannot be identified by fingerprints

[0007] (3) During the process of fingerprint collection, factors such as the twisting and stretching of the finger press, and the pressing force will cause the fingerprint to deform and rotate, resulting in a lack of constancy in the description of the feature, which will make the description of the registered feature value and the feature value of the verification The description is different, so the possibility of successful comparison will be reduced. This kind of situation is relatively common in reality.

Therefore, when designing a verification system, these factors must be taken into account, so that the reliability of the system will deteriorate and the complexity will increase, which will increase the rejection rate

[0008] (4) Although it is difficult to steal biometrics, the possibility exists

[0009] The fingerprint identification method still only stays on the "appearance" of observing people's biological characteristics, and the safety factor is low

Images