CN111223210A - Intelligent lock fingerprint identification system - Google Patents

Intelligent lock fingerprint identification system Download PDF

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Publication number
CN111223210A
CN111223210A CN201911132396.4A CN201911132396A CN111223210A CN 111223210 A CN111223210 A CN 111223210A CN 201911132396 A CN201911132396 A CN 201911132396A CN 111223210 A CN111223210 A CN 111223210A
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fingerprint
image
intelligent lock
point
fingerprint identification
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Chinese (zh)
Inventor
周秀凯
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Zhejiang Yintejia Intelligent Technology Co ltd
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Zhejiang Yintejia Intelligent Technology Co ltd
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C9/00—Individual registration on entry or exit
    • G07C9/00174—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys
    • G07C9/00563—Electronically operated locks; Circuits therefor; Nonmechanical keys therefor, e.g. passive or active electrical keys or other data carriers without mechanical keys using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30164—Workpiece; Machine component

Abstract

The invention relates to the technical field of intelligent lock equipment, in particular to an intelligent lock fingerprint identification system which comprises an intelligent lock seat, wherein a fingerprint identification lock and a door handle are arranged on the intelligent lock seat, the fingerprint identification lock comprises a fingerprint extraction module, an image preprocessing module, a feature extraction module, a fingerprint library and a matching identification module, mounting holes are formed in four top corners of the intelligent lock seat, and a mechanical key hole is formed in the intelligent lock seat. According to the invention, a grey scale segmentation method is adopted to segment the challenge image, the fingerprint image is processed by a self-adaptive binarization method, and finally the image is refined and the interferences such as burrs, fractures and the like are removed, so that the identification accuracy is improved; the point matching algorithm based on the structural characteristics is adopted to match the calibrated point sets, the number of matched characteristic points can be judged to be successfully matched within the range of sixty-five percent of the ratio of the two point sets, the identification speed is high, the unlocking efficiency is high, and the use is more convenient.

Description

Intelligent lock fingerprint identification system
Technical Field
The invention relates to the technical field of intelligent lock equipment, in particular to an intelligent lock fingerprint identification system.
Background
With the rapid development of the intelligent door lock industry, the intelligent fingerprint lock gradually goes deep into the daily life of people. Along with the development of science and technology, anti-theft device is also more and more scientific and technological, and fingerprint trick lock has obtained ripe application on the burglary-resisting door, and fingerprint identification speed on the present intelligent lock is slow, and the effect is poor.
Disclosure of Invention
The invention aims to provide an intelligent lock fingerprint identification system to solve the problems of low fingerprint identification speed and poor effect of the prior art in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
the intelligent lock fingerprint identification system comprises an intelligent lock seat, wherein a fingerprint identification lock and a door handle are arranged on the intelligent lock seat, and the fingerprint identification lock comprises a fingerprint extraction module, an image preprocessing module, a feature extraction module, a fingerprint library and a matching identification module.
Preferably, the four top corners of the intelligent lock seat are provided with mounting holes.
Preferably, the intelligent lock seat is further provided with a mechanical key hole.
Preferably, the operation flow of the image preprocessing module includes the following steps:
s1 fingerprint image acquisition: fingerprint information is acquired by adopting a photoelectric, silicon chip or ultrasonic fingerprint acquisition sheet;
s2 fingerprint image segmentation: performing primary processing on the image according to the gray scale, and performing normalization and segmentation processing on the fingerprint image after the primary processing to eliminate the remaining background area;
s3 fingerprint image enhancement: filtering and denoising the fingerprint image, enhancing the contrast ratio of ridge-valley line structures, simultaneously inhibiting noise, connecting broken ridges and separating adhered ridges, keeping information according to the specific requirement of highlighting an image, and weakening or removing unnecessary information;
s4 binarization of image: the gray level image is converted into an image with only black and white gray levels, so that the whole image is simplified into binary information in fingerprint identification, the image information is compressed, the main information of the lines is reserved, the storage space is saved, and a large amount of adhesion is removed;
s5 image thinning: the edge pixels of the fingerprint lines are deleted to enable the fingerprint lines to be only one pixel wide, redundant information is reduced, main features of the fingerprint lines are highlighted, the connectivity, the directionality and the feature points of the lines are kept unchanged during thinning, and meanwhile the centers of the lines are kept unchanged.
Preferably, the fingerprint image segmentation performs normalization processing on the fingerprint image after the preliminary processing, and the normalization processing is performed by using the following formula:
Figure BDA0002278684330000021
in the above formula, if
Figure BDA0002278684330000022
Then the gray value is adjusted
Figure BDA0002278684330000023
Normalized 255 background processing, where M0And V0For the desired mean and variance, MiAnd ViMean and variance of the fingerprint image.
Preferably, the fingerprint image segmentation divides the fingerprint image into 8 × 8 small blocks, if the fingerprint image is in a background region, the variance of the gray scale is small, and the variance of the fingerprint image in a foreground region is large, the variance of each small block is calculated, a threshold value is further set, a square region smaller than the threshold value is set as the background region, the gray scale value of the square region is set as 255, the gray scale value of the square region larger than the threshold value is kept unchanged, and the fingerprint image is separated from the background region.
Preferably, the transformation function of the image binarization is expressed by the following formula:
Figure BDA0002278684330000024
wherein T is a designated threshold value and x is a gray value.
Preferably, the feature extraction module adopts a feature extraction algorithm, the feature extraction algorithm is specifically a template matching method, the minutiae features of the fingerprint, namely, end points and bifurcation points are extracted, the end points and the bifurcation points are established on the basis of statistical analysis of 8 neighboring points, and in all states of eight neighborhoods, 8 types of feature conditions are satisfied, and 9 types of feature conditions are satisfied.
Preferably, the algorithm of the template matching method is as follows:
s1: starting from the end point, the eight neighborhoods of the end point only have one black point, and the black point is the next point tracked by the ridge line;
s2: for the continuous points in the middle of the ridge line, only two black points are arranged in eight neighborhoods, the last tracked point is removed, and the next point is the next point to be tracked;
s3: let set Ω ═ xi,yi,zi,giRecord the abscissa x of the endpoint or bifurcation pointiOrdinate yiAnd type z of feature pointi,giIf the number of black points in the eight neighborhoods of the tracked point is equal to 1 and the number of intersections is equal to 2, the following formula is adopted:
Figure BDA0002278684330000031
then the endpoint is considered;
if the number of the eight neighborhood black points of the tracked point is equal to 3 and the number of intersections is equal to 6, the following formula is given:
Figure BDA0002278684330000032
considering the angle of the end point as the angle of the end line taking the end point as the starting point, and taking the angle of the relative minimum branch as the angle of the bifurcation point;
the angle of the end line and the branch line is calculated as follows: starting from the position of a feature, the coordinates are (x)i,vi) The step size of 7 is searched, the coordinate of the last point is (x, y), and the formula is as follows: gi ═ arctan (y-y)i)/(x-xi)。
Preferably, the judgment basis of the matching identification module is a matching success rate Q, and the formula is as follows: if Q is (total number of matches/total number of extracted features) × 100%, Q reaches 33% or more, the matching is successful.
Compared with the prior art, the invention has the beneficial effects that:
1. the intelligent lock fingerprint identification system adopts a gray segmentation method to segment a challenge image, processes the fingerprint image by a self-adaptive binarization method, and finally refines the image and removes the interferences such as burrs, fractures and the like, thereby improving the identification accuracy; the point matching algorithm based on the structural characteristics is adopted to match the calibrated point sets, the number of matched characteristic points can be judged to be successfully matched within the range of sixty-five percent of the ratio of the two point sets, the identification speed is high, the unlocking efficiency is high, and the use is more convenient.
2. The mounting holes have all been seted up to four apex angle departments of this intelligence lock fingerprint identification system intelligence lock seat, through the self-tapping screw with mounting hole looks adaptation, can install the intelligence lock seat, still is provided with mechanical key hole on the intelligence lock seat, and the key through the mechanical key hole that sets up and looks adaptation can use after fingerprint identification lock became invalid.
Drawings
FIG. 1 is a schematic view of the present invention in use;
FIG. 2 is a schematic diagram of a fingerprint recognition process according to the present invention;
FIG. 3 is a schematic view of an embodiment of the image preprocessing of the present invention;
FIG. 4 is a schematic diagram illustrating a comparison of fingerprint image acquisition modes according to the present invention.
In the figure: 1. an intelligent lock seat; 2. mounting holes; 3. a door handle; 4. a fingerprint identification lock; 41. a fingerprint extraction module; 42. an image preprocessing module; 43. a feature extraction module; 44. a fingerprint library; 45. a fingerprint feature value; 46. a matching identification module; 5. a mechanical key hole.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the equipment or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Example 1
An intelligent lock fingerprint identification system is shown in fig. 1, and comprises an intelligent lock base 1, wherein a fingerprint identification lock 4 and a door handle 3 are arranged on the intelligent lock base 1, and as shown in fig. 2, the fingerprint identification lock 4 comprises a fingerprint extraction module 41, an image preprocessing module 42, a feature extraction module 43, a fingerprint library 44 and a matching identification module 46.
Further, mounting hole 2 has all been seted up to four apex angle departments of intelligence lock seat 1, through the self tapping screw with 2 looks adaptations of mounting hole, can install intelligence lock seat 1, still is provided with mechanical key hole 5 on the intelligence lock seat 1, and the key through the mechanical key hole 5 that sets up and looks adaptation can use after fingerprint identification lock 4 became invalid.
Specifically, as shown in fig. 3, the operation flow of the image preprocessing module 42 includes the following steps:
s1 fingerprint image acquisition: as shown in fig. 4, a photoelectric, silicon chip or ultrasonic fingerprint acquisition sheet is used to acquire fingerprint information;
s2 fingerprint image segmentation: performing primary processing on the image according to the gray scale, and performing normalization and segmentation processing on the fingerprint image after the primary processing to eliminate the remaining background area;
s3 fingerprint image enhancement: filtering and denoising the fingerprint image, enhancing the contrast ratio of ridge-valley line structures, simultaneously inhibiting noise, connecting broken ridges and separating adhered ridges, keeping information according to the specific requirement of highlighting an image, and weakening or removing unnecessary information;
s4 binarization of image: the gray level image is converted into an image with only black and white gray levels, so that the whole image is simplified into binary information in fingerprint identification, the image information is compressed, the main information of the lines is reserved, the storage space is saved, and a large amount of adhesion is removed;
s5 image thinning: the edge pixels of the fingerprint lines are deleted to enable the fingerprint lines to be only one pixel wide, redundant information is reduced, main features of the fingerprint lines are highlighted, the connectivity, the directionality and the feature points of the lines are kept unchanged during thinning, and meanwhile the centers of the lines are kept unchanged.
The fingerprint image segmentation is used for carrying out normalization processing on the fingerprint image after the primary processing, and the following formula is utilized:
Figure BDA0002278684330000051
in the above formula, if
Figure BDA0002278684330000061
Then the gray value is adjusted
Figure BDA0002278684330000062
Normalized 255 background processing, where M0And V0For the desired mean and variance, MiAnd ViMean and variance of the fingerprint image.
The intelligent lock fingerprint identification system provided by the invention adopts a gray segmentation method to segment the challenge image. Denoising by using median filtering, processing the fingerprint image by a self-adaptive binarization method, and finally refining the image and removing the interference such as burrs, fractures and the like; and matching the calibrated point sets by adopting a point matching algorithm based on structural features, wherein the matching can be judged to be successful when the number of matched feature points accounts for about sixty-five percent of the two point sets.
Specifically, the fingerprint image segmentation divides the fingerprint image into 8 × 8 small blocks, if the fingerprint image is in a background area, the variance of the gray scale is small, the variance of the fingerprint image in a foreground area is large, the variance of each small block is calculated, a threshold is set again, a square area smaller than the threshold is set as the background area, the gray scale value of the square area is set as 255, the gray scale value of the square area larger than the threshold is kept unchanged, and the fingerprint image is separated from the background area.
The transformation function of image binarization is expressed by the following formula:
Figure BDA0002278684330000063
wherein T is a designated threshold value and x is a gray value.
The feature extraction module adopts a feature extraction algorithm, the feature extraction algorithm is specifically a template matching method, and the detail features of the fingerprint, namely end points and bifurcation points are extracted, the end points and the bifurcation points are established on the basis of statistical analysis of 8 adjacent points, so that in all states of eight neighborhoods, 8 types of feature conditions are met, and 9 types of feature conditions are met.
It should be noted that the algorithm of the template matching method is specifically as follows:
s1: starting from the end point, the eight neighborhoods of the end point only have one black point, and the black point is the next point tracked by the ridge line;
s2: for the continuous points in the middle of the ridge line, only two black points are arranged in eight neighborhoods, the last tracked point is removed, and the next point is the next point to be tracked;
s3: let set Ω ═ xi,yi,zi,giRecord the abscissa x of the endpoint or bifurcation pointiOrdinate yiAnd type z of feature pointi,giIf the number of black points in the eight neighborhoods of the tracked point is equal to 1 and the number of intersections is equal to 2, the following formula is adopted:
Figure BDA0002278684330000071
then the endpoint is considered;
if the number of the eight neighborhood black points of the tracked point is equal to 3 and the number of intersections is equal to 6, the following formula is given:
Figure BDA0002278684330000072
considering the angle of the end point as the angle of the end line taking the end point as the starting point, and taking the angle of the relative minimum branch as the angle of the bifurcation point;
the angle of the end line and the branch line is calculated as follows: fromThe starting coordinates of the position of a feature are (x)i,vi) The step size of 7 is searched, the coordinate of the last point is (x, y), and the formula is as follows: gi ═ arctan (y-y)i)/(x-xi)。
The judgment basis of the matching identification module is a matching success rate Q, and the formula is as follows: if Q is (total number of matches/total number of extracted features) × 100%, Q reaches 33% or more, the matching is successful.
The intelligent lock fingerprint identification system provided by the invention adopts a gray segmentation method to segment a challenge image, processes the fingerprint image by a self-adaptive binarization method, and finally refines the image and removes interferences such as burrs, fractures and the like, so that the identification accuracy is improved; the point matching algorithm based on the structural characteristics is adopted to match the calibrated point sets, the number of matched characteristic points can be judged to be successfully matched within the range of sixty-five percent of the ratio of the two point sets, the identification speed is high, the unlocking efficiency is high, and the use is more convenient.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. The utility model provides an intelligence lock fingerprint identification system which characterized in that: the intelligent lock comprises an intelligent lock seat (1), wherein a fingerprint identification lock (4) and a door handle (3) are arranged on the intelligent lock seat (1), and the fingerprint identification lock (4) comprises a fingerprint extraction module (41), an image preprocessing module (42), a feature extraction module (43), a fingerprint library (44) and a matching identification module (46).
2. The intelligent lock fingerprint identification system of claim 1, wherein: four apex angle departments of intelligence lock seat (1) have all seted up mounting hole (2).
3. The intelligent lock fingerprint identification system of claim 1, wherein: the intelligent lock seat (1) is also provided with a mechanical key hole (5).
4. The intelligent lock fingerprint identification system of claim 1, wherein: the operation flow of the image preprocessing module (42) comprises the following steps:
s1 fingerprint image acquisition: fingerprint information is acquired by adopting a photoelectric, silicon chip or ultrasonic fingerprint acquisition sheet;
s2 fingerprint image segmentation: performing primary processing on the image according to the gray scale, and performing normalization and segmentation processing on the fingerprint image after the primary processing to eliminate the remaining background area;
s3 fingerprint image enhancement: filtering and denoising the fingerprint image, enhancing the contrast ratio of ridge-valley line structures, simultaneously inhibiting noise, connecting broken ridges and separating adhered ridges, keeping information according to the specific requirement of highlighting an image, and weakening or removing unnecessary information;
s4 binarization of image: the gray level image is converted into an image with only black and white gray levels, so that the whole image is simplified into binary information in fingerprint identification, the image information is compressed, the main information of the lines is reserved, the storage space is saved, and a large amount of adhesion is removed;
s5 image thinning: the edge pixels of the fingerprint lines are deleted to enable the fingerprint lines to be only one pixel wide, redundant information is reduced, main features of the fingerprint lines are highlighted, the connectivity, the directionality and the feature points of the lines are kept unchanged during thinning, and meanwhile the centers of the lines are kept unchanged.
5. The intelligent lock fingerprint identification system of claim 1, wherein: the fingerprint image segmentation is used for carrying out normalization processing on the fingerprint image after the primary processing, and the following formula is utilized:
Figure FDA0002278684320000021
in the above formula, if
Figure FDA0002278684320000022
Then the gray value is adjusted
Figure FDA0002278684320000023
Normalized 255 background processing, where M0And V0For the desired mean and variance, MiAnd ViMean and variance of the fingerprint image.
6. The intelligent lock fingerprint identification system of claim 1, wherein: the fingerprint image is divided into 8-by-8 small blocks by the fingerprint image segmentation, if the fingerprint image is in a background area, the variance of the gray scale is small, the variance of the fingerprint image in a foreground area is large, the variance of each small block is calculated, a threshold value is set again, a square block area smaller than the threshold value is set as the background area, the gray scale value of the square block area is set as 255, the gray scale value of the square block area larger than the threshold value is kept unchanged, and the fingerprint image is separated from the background area.
7. The intelligent lock fingerprint identification system of claim 1, wherein: the transformation function of image binarization is expressed by the following formula:
Figure FDA0002278684320000024
wherein T is a designated threshold value and x is a gray value.
8. The intelligent lock fingerprint identification system of claim 1, wherein: the feature extraction module (43) adopts a feature extraction algorithm, the feature extraction algorithm is specifically a template matching method, and the detail features of the fingerprint, namely end points and bifurcation points are extracted, the end points and the bifurcation points are established on the basis of statistical analysis of 8 adjacent points, so that in all states of eight neighborhoods, 8 types of feature conditions are satisfied, and 9 types of feature conditions are satisfied.
9. The intelligent lock fingerprint identification system of claim 1, wherein: the algorithm of the template matching method is as follows:
s1: starting from the end point, the eight neighborhoods of the end point only have one black point, and the black point is the next point tracked by the ridge line;
s2: for the continuous points in the middle of the ridge line, only two black points are arranged in eight neighborhoods, the last tracked point is removed, and the next point is the next point to be tracked;
s3: let set Ω ═ xi,yi,zi,giRecord the abscissa x of the endpoint or bifurcation pointiOrdinate yiAnd type z of feature pointi,giIf the number of black points in the eight neighborhoods of the tracked point is equal to 1 and the number of intersections is equal to 2, the following formula is adopted:
Figure FDA0002278684320000031
then the endpoint is considered;
if the number of the eight neighborhood black points of the tracked point is equal to 3 and the number of intersections is equal to 6, the following formula is given:
Figure FDA0002278684320000032
considering the angle of the end point as the angle of the end line taking the end point as the starting point, and taking the angle of the relative minimum branch as the angle of the bifurcation point;
the angle of the end line and the branch line is calculated as follows: starting from the position of a feature, the coordinates are (x)i,vi) The step size of 7 is searched, the coordinate of the last point is (x, y), and the formula is as follows: gi ═ arctan (y-y)i)/(x-xi)。
10. The intelligent lock fingerprint identification system of claim 1, wherein: the judgment basis of the matching identification module (46) is the matching success rate Q, and the formula is as follows: if Q is (total number of matches/total number of extracted features) × 100%, Q reaches 33% or more, the matching is successful.
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CN111914755A (en) * 2020-08-03 2020-11-10 河南大学 Eight-direction gradient-solving fingerprint identification model

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Publication number Priority date Publication date Assignee Title
CN111882715A (en) * 2020-07-29 2020-11-03 金陵科技学院 Method for intelligent trunk locking system based on ant colony algorithm
CN111914755A (en) * 2020-08-03 2020-11-10 河南大学 Eight-direction gradient-solving fingerprint identification model

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Application publication date: 20200602