CN114399796A - Fingerprint identification method, device, terminal and storage medium - Google Patents
Fingerprint identification method, device, terminal and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a fingerprint identification method, a fingerprint identification device, a terminal and a storage medium, wherein the method comprises the following steps: acquiring an original fingerprint image; performing image enhancement processing on the original fingerprint image to generate a first image; determining end points and cross points of fingerprint ridges on the first image; determining inflection points on the fingerprint ridge based on the end points and the cross points; performing feature dimensionality reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector subjected to dimensionality reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image; and performing fingerprint identification based on the feature vector. In this scheme, come with extreme point and cross point through the flex point that increases the fingerprint ridge, richened the fingerprint characteristic for the fingerprint characteristic is more reasonable, and falls the dimension to flex point, extreme point and cross point, with this can reduce the memory and improve the speed of comparing when fingerprint identification.
Description
Technical Field
The present invention relates to the field of fingerprint identification technologies, and in particular, to a method, an apparatus, a terminal, and a storage medium for fingerprint identification.
Background
The fingerprint identification mainly extracts key characteristic information in a fingerprint image, and describes and registers the characteristic information.
At present, fingerprint identification, especially small fingerprint identification, is applied in many fields, and in order to improve the precision, the amount of extracted information of the small-array fingerprint image features is generally large, which results in long time consumption. Specifically, in the field of current small-area-array fingerprint registration, registration methods such as extreme points, ridge lines and minutiae are mainly used. The extreme point registration extracts a lot of information, the matching precision is high, but the time and the memory consumption are high, and certain requirements are imposed on equipment; the ridge line characteristics describe the texture characteristics of the fingerprint, but lack the gray information of the original fingerprint image; the characteristics of the minutiae are relatively accurate, but the characteristic quantity of the minutiae on a small area array is small, so that the final accuracy is affected.
Thus, there is a need for a better method to solve the problems of the prior art.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, a terminal and a storage medium for fingerprint identification, so as to solve the problems in the prior art.
Specifically, the present invention proposes the following specific examples:
the embodiment of the invention provides a fingerprint identification method, which comprises the following steps:
acquiring an original fingerprint image;
performing image enhancement processing on the original fingerprint image to generate a first image;
determining end points and cross points of fingerprint ridges on the first image;
determining inflection points on the fingerprint ridge based on the end points and the cross points;
performing feature dimensionality reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector subjected to dimensionality reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image;
and performing fingerprint identification based on the feature vector.
In a specific embodiment, the image enhancement process includes: one or more of a gradation raising process, a gradation enhancing process, a directional filtering process, a binarization process, and an image thinning process.
In a specific embodiment, the determining an inflection point on the fingerprint ridge based on the endpoint and the cross point includes:
if the fingerprint ridge does not contain the cross point and the length of the fingerprint ridge exceeds a preset length threshold, setting the fingerprint ridge as a ridge to be processed;
if the fingerprint ridge line comprises the cross point, dividing the fingerprint ridge line into three divided ridge lines based on the cross point; if the length of the segmentation ridge line exceeds a preset length threshold value, setting the segmentation ridge line as a ridge line to be processed;
for each ridge line to be processed, if an included angle between a first connecting line from a middle point to a first point and a second connecting line from the middle point to a second point is in a preset angle range, determining the middle point as an inflection point; the first point is a point close to an end point on one side of the ridge line to be processed; the second point is a point close to the end point on the other side of the ridge line to be processed; the distance between the first point and the close end point and the distance between the second point and the close end point are both larger than a preset distance threshold; the first point and the second point are both at the same distance from the intermediate point.
In a specific embodiment, the performing feature dimensionality reduction on the end point, the cross point, and the inflection point on the second image to obtain a feature vector after the dimension reduction includes:
aligning the end point, the cross point and the inflection point on a second image;
determining a feature descriptor of an area where the endpoint, the cross point and the inflection point which are aligned are located;
and performing feature dimensionality reduction in a mode of multiplying a preset projection matrix by the feature descriptor to obtain a feature vector after dimensionality reduction.
In a specific embodiment, the aligning the end point, the cross point and the inflection point on the second image includes:
determining the preset directions of the end point, the cross point and the inflection point on a second image; the preset directions of the end point, the fork point and the inflection point are different;
and rotating the areas where the end point, the fork point and the inflection point are located based on the direction, so that the areas where the end point, the fork point and the inflection point are located face to the same direction, and alignment is realized.
The embodiment of the invention also provides a fingerprint identification device, which comprises:
the acquisition module is used for acquiring an original fingerprint image;
the image enhancement module is used for carrying out image enhancement processing on the original fingerprint image to generate a first image;
the determining module is used for determining the end point and the cross point of the fingerprint ridge line on the first image;
a knee module to determine a knee on the fingerprint ridge based on the endpoint and the cross-point;
the dimension reduction module is used for performing feature dimension reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector after dimension reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image;
and the fingerprint identification module is used for carrying out fingerprint identification based on the characteristic vector.
In a specific embodiment, the image enhancement process includes: one or more of a gradation raising process, a gradation enhancing process, a directional filtering process, a binarization process, and an image thinning process.
In a specific embodiment, the inflection module is configured to:
if the fingerprint ridge does not contain the cross point and the length of the fingerprint ridge exceeds a preset length threshold, setting the fingerprint ridge as a ridge to be processed;
if the fingerprint ridge line comprises the cross point, dividing the fingerprint ridge line into three divided ridge lines based on the cross point; if the length of the segmentation ridge line exceeds a preset length threshold value, setting the segmentation ridge line as a ridge line to be processed;
for each ridge line to be processed, if an included angle between a first connecting line from a middle point to a first point and a second connecting line from the middle point to a second point is in a preset angle range, determining the middle point as an inflection point; the first point is a point close to an end point on one side of the ridge line to be processed; the second point is a point close to the end point on the other side of the ridge line to be processed; the distance between the first point and the close end point and the distance between the second point and the close end point are both larger than a preset distance threshold; the first point and the second point are both at the same distance from the intermediate point.
The embodiment of the present invention further provides a terminal, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the fingerprint identification method when executing the computer program.
The embodiment of the present invention further provides a storage medium, in which a computer program is stored, and the computer program, when executed, implements the fingerprint identification method described above.
Therefore, the embodiment of the invention provides a fingerprint identification method, a fingerprint identification device, a terminal and a storage medium, wherein the method comprises the following steps: acquiring an original fingerprint image; performing image enhancement processing on the original fingerprint image to generate a first image; determining end points and cross points of fingerprint ridges on the first image; determining inflection points on the fingerprint ridge based on the end points and the cross points; performing feature dimensionality reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector subjected to dimensionality reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image; and performing fingerprint identification based on the feature vector. In this scheme, come with extreme point and cross point through the flex point that increases the fingerprint ridge, richened the fingerprint characteristic for the fingerprint characteristic is more reasonable, and falls the dimension to flex point, extreme point and cross point, with this can reduce the memory and improve the speed of comparing when fingerprint identification.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings required to be used in the embodiments will be briefly described below, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of the present invention. Like components are numbered similarly in the various figures.
Fig. 1 is a schematic flow chart illustrating a method for fingerprint identification according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating end points and cross points of a fingerprint image in a fingerprint identification method according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a method for fingerprint identification according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for fingerprint identification according to an embodiment of the present invention.
Illustration of the drawings:
1-endpoint; 2-a cross point;
201-an acquisition module; 202-an image enhancement module; 203-a determination module; 204-inflection module;
205-dimension reduction module; 206-fingerprint recognition module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in 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.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present invention, are only intended to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
Example 1
The embodiment 1 of the invention discloses a fingerprint identification method, which comprises the following steps as shown in figure 1:
s101, acquiring an original fingerprint image;
specifically, the original fingerprint image is also the image of the fingerprint acquired by the fingerprint input device.
After the original fingerprint image is obtained, the original fingerprint image is subjected to smooth denoising to obtain a second image, and step S102 is executed to obtain a first image.
Step S102, carrying out image enhancement processing on the original fingerprint image to generate a first image;
specifically, the image enhancement processing includes: one or more of a gradation raising process, a gradation enhancing process, a directional filtering process, a binarization process, and an image thinning process.
Specifically, the gray level raising processing is used for raising the gray level of the original fingerprint image to 0-255, and the pixel level range is expanded; the gray level enhancement processing is used for enhancing the original fingerprint image according to the difference value of the original fingerprint image and a smooth image (the smooth image is obtained by smoothing the original fingerprint image); the directional filtering process is to calculate the pixel direction value in a certain size square (8 x 8) point by point to obtain a directional diagram, then traverse each pixel, and perform directional smoothing process and then sharpening process on the current pixel.
Step S103, determining the end point and the cross point of the fingerprint ridge line on the first image;
specifically, the fingerprint ridge is shown in fig. 2, where two ends of the fingerprint ridge are endpoints 1 of endpoint 1, and if an intersection occurs, the intersection is also referred to as a cross point 2; according to practical experience, only the intersection of three forks, namely the fork 2 in fig. 2, appears on the fingerprint ridge line in the fingerprint image, and other numbers of intersections do not appear.
Step S104, determining inflection points on the fingerprint ridge line based on the end points and the cross points;
specifically, as shown in fig. 3, the determining an inflection point on the fingerprint ridge line based on the endpoint and the crossing point in step S104 includes:
if the fingerprint ridge does not contain the cross point and the length of the fingerprint ridge exceeds a preset length threshold, setting the fingerprint ridge as a ridge to be processed;
if the fingerprint ridge line comprises the cross point, dividing the fingerprint ridge line into three divided ridge lines based on the cross point; if the length of the segmentation ridge line exceeds a preset length threshold value, setting the segmentation ridge line as a ridge line to be processed;
for each ridge line to be processed, if an included angle between a first connecting line from a middle point to a first point and a second connecting line from the middle point to a second point is in a preset angle range, determining the middle point as an inflection point; the first point is a point close to an end point on one side of the ridge line to be processed; the second point is a point close to the end point on the other side of the ridge line to be processed; the distance between the first point and the approached end point (specifically, the distance on the ridge line) and the distance between the second point and the approached end point (specifically, the distance on the ridge line) are both larger than a preset distance threshold; the first point and the second point are both at the same distance from the intermediate point.
Specifically, tracing a ridge line from an end point, extracting an inflection point on the ridge line with a length greater than a predetermined length (d is 17-20), assuming that B is a current tracing point, calculating a change angle before and after the inflection point, namely an included angle between AB and AC, at a pixel point a with a length d1 (for example, 6-7) before B and a pixel point C with a length d2 (for example, 6-7) after B, if the change of the angle is too small, the inflection point is not found, and if the change of the angle reaches a certain value, the inflection point B is determined; and finally, screening inflection points with close distance, low quality and smaller angle change. The specific angle threshold for judging whether the angle is large or small can be set according to actual conditions.
Step S105, performing feature dimensionality reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector after the dimensionality reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image;
specifically, the performing feature dimensionality reduction on the endpoint, the cross point, and the inflection point on the second image in step S105 to obtain a feature vector after the feature dimensionality reduction includes:
aligning the end point, the cross point and the inflection point on a second image;
determining a feature descriptor of an area where the endpoint, the cross point and the inflection point which are aligned are located;
and performing feature dimensionality reduction in a mode of multiplying a preset projection matrix by the feature descriptor to obtain a feature vector after dimensionality reduction.
Wherein aligning the end point, the cross point and the inflection point on the second image comprises:
determining the preset directions of the end point, the cross point and the inflection point on a second image; the preset directions of the end point, the fork point and the inflection point are different;
and rotating the areas where the end point, the fork point and the inflection point are located based on the direction, so that the areas where the end point, the fork point and the inflection point are located face to the same direction, and alignment is realized.
Specifically, the direction of the endpoint, the fork point and the inflection point is determined firstly, wherein the direction of the endpoint is the direction from the endpoint to a point which is d distance away from the ridge line of the current position; the direction of the cross point is: the fork point position points to the direction of the midpoint position of the two ridge lines with smaller angles; the direction of the inflection point is: the bisector direction of the angle between BA and BC (two in this case the smaller one).
After the direction is determined, the end point, the cross point and the inflection point are all set as feature points, the feature points (including 15 × 15 pixels in the surrounding block) are rotated to the same direction, the regional feature descriptor is calculated, and then the regional feature descriptor is multiplied by a projection matrix for dimensionality reduction to obtain the dimensionality reduction feature.
In particular, the projection matrix is calculated in advance from a large amount of data (feature descriptors). For example, the calculation method includes calculating 15000 feature point descriptors as training samples to form an original feature matrix 15000 x 450, calculating a covariance matrix N of the matrix, calculating feature vectors of the covariance matrix N, sorting according to the size of feature roots, selecting the top N feature vectors (N < 450, the more top the feature value energy percentage is larger), and forming a projection matrix T.
And S106, fingerprint identification is carried out based on the feature vector.
According to the scheme, the inflection point characteristic quantity required to be determined is small, the effect is good, the position of the characteristic salient in the ridge line is high in accuracy and small in quantity compared with the extreme point, all ridge line information is stored relative to the ridge line characteristic, only important characteristics are extracted, the memory and redundancy are reduced, the dimension reduction size of the characteristic descriptor is 10-22, the dimension reduction size is reduced by 20-30 times compared with the original size memory, the separability is achieved through distance comparison, the effect is good, the memory is reduced, and the comparison speed is increased.
Example 2
For further explanation of the present solution, embodiment 2 of the present invention further discloses a fingerprint identification apparatus, as shown in fig. 4, including:
an obtaining module 201, configured to obtain an original fingerprint image;
the image enhancement module 202 is used for performing image enhancement processing on the original fingerprint image to generate a first image;
a determining module 203, configured to determine end points and cross points of fingerprint ridges on the first image;
a inflection module 204 for determining an inflection point on the fingerprint ridge based on the endpoint and the cross point;
a dimension reduction module 205, configured to perform feature dimension reduction on the endpoint, the cross point, and the inflection point on a second image to obtain a feature vector after the dimension reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image;
a fingerprint identification module 206, configured to perform fingerprint identification based on the feature vector.
In a specific embodiment, the image enhancement process includes: one or more of a gradation raising process, a gradation enhancing process, a directional filtering process, a binarization process, and an image thinning process.
In a specific embodiment, the inflection module 204 is configured to:
if the fingerprint ridge does not contain the cross point and the length of the fingerprint ridge exceeds a preset length threshold, setting the fingerprint ridge as a ridge to be processed;
if the fingerprint ridge line comprises the cross point, dividing the fingerprint ridge line into three divided ridge lines based on the cross point; if the length of the segmentation ridge line exceeds a preset length threshold value, setting the segmentation ridge line as a ridge line to be processed;
for each ridge line to be processed, if an included angle between a first connecting line from a middle point to a first point and a second connecting line from the middle point to a second point is in a preset angle range, determining the middle point as an inflection point; the first point is a point close to an end point on one side of the ridge line to be processed; the second point is a point close to the end point on the other side of the ridge line to be processed; the distance between the first point and the close end point and the distance between the second point and the close end point are both larger than a preset distance threshold; the first point and the second point are both at the same distance from the intermediate point.
In a specific embodiment, the dimension reduction module 205 is configured to:
aligning the end point, the cross point and the inflection point on a second image;
determining a feature descriptor of an area where the endpoint, the cross point and the inflection point which are aligned are located;
and performing feature dimensionality reduction in a mode of multiplying a preset projection matrix by the feature descriptor to obtain a feature vector after dimensionality reduction.
In a specific embodiment, the dimension reduction module 205 performs an alignment process on the end point, the cross point and the inflection point on the second image, including:
determining the preset directions of the end point, the cross point and the inflection point on a second image; the preset directions of the end point, the fork point and the inflection point are different;
and rotating the areas where the end point, the fork point and the inflection point are located based on the direction, so that the areas where the end point, the fork point and the inflection point are located face to the same direction, and alignment is realized.
Example 3
The embodiment 3 of the present invention further discloses a terminal, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the fingerprint identification method described in the embodiment 1 when executing the computer program.
Specifically, the terminal may be a fingerprint identification device.
Example 4
The embodiment 4 of the present invention further discloses a storage medium, in which a computer program is stored, and the computer program implements the fingerprint identification method described in the embodiment 1 when executed.
Therefore, the embodiment of the invention provides a fingerprint identification method, a fingerprint identification device, a terminal and a storage medium, wherein the method comprises the following steps: acquiring an original fingerprint image; performing image enhancement processing on the original fingerprint image to generate a first image; determining end points and cross points of fingerprint ridges on the first image; determining inflection points on the fingerprint ridge based on the end points and the cross points; performing feature dimensionality reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector subjected to dimensionality reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image; and performing fingerprint identification based on the feature vector. In this scheme, come with extreme point and cross point through the flex point that increases the fingerprint ridge, richened the fingerprint characteristic for the fingerprint characteristic is more reasonable, and falls the dimension to flex point, extreme point and cross point, with this can reduce the memory and improve the speed of comparing when fingerprint identification.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part of the technical solution that contributes to the prior art in essence can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A method of fingerprint recognition, comprising:
acquiring an original fingerprint image;
performing image enhancement processing on the original fingerprint image to generate a first image;
determining end points and cross points of fingerprint ridges on the first image;
determining inflection points on the fingerprint ridge based on the end points and the cross points;
performing feature dimensionality reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector subjected to dimensionality reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image;
and performing fingerprint identification based on the feature vector.
2. The method of claim 1, wherein the image enhancement processing comprises: one or more of a gradation raising process, a gradation enhancing process, a directional filtering process, a binarization process, and an image thinning process.
3. The method of claim 1, wherein the determining inflection points on the fingerprint ridge based on the endpoint and the cross point comprises:
if the fingerprint ridge does not contain the cross point and the length of the fingerprint ridge exceeds a preset length threshold, setting the fingerprint ridge as a ridge to be processed;
if the fingerprint ridge line comprises the cross point, dividing the fingerprint ridge line into three divided ridge lines based on the cross point; if the length of the segmentation ridge line exceeds a preset length threshold value, setting the segmentation ridge line as a ridge line to be processed;
for each ridge line to be processed, if an included angle between a first connecting line from a middle point to a first point and a second connecting line from the middle point to a second point is in a preset angle range, determining the middle point as an inflection point; the first point is a point close to an end point on one side of the ridge line to be processed; the second point is a point close to the end point on the other side of the ridge line to be processed; the distance between the first point and the close end point and the distance between the second point and the close end point are both larger than a preset distance threshold; the first point and the second point are both at the same distance from the intermediate point.
4. The method of claim 1, wherein the performing a feature dimensionality reduction on the end point, the cross point, and the inflection point on the second image to obtain a reduced-dimensionality feature vector comprises:
aligning the end point, the cross point and the inflection point on a second image;
determining a feature descriptor of an area where the endpoint, the cross point and the inflection point which are aligned are located;
and performing feature dimensionality reduction in a mode of multiplying a preset projection matrix by the feature descriptor to obtain a feature vector after dimensionality reduction.
5. The method of claim 1, wherein aligning the endpoint, the cross-point, and the inflection point on a second image comprises:
determining the preset directions of the end point, the cross point and the inflection point on a second image; the preset directions of the end point, the fork point and the inflection point are different;
and rotating the areas where the end point, the fork point and the inflection point are located based on the direction, so that the areas where the end point, the fork point and the inflection point are located face to the same direction, and alignment is realized.
6. An apparatus for fingerprint recognition, comprising:
the acquisition module is used for acquiring an original fingerprint image;
the image enhancement module is used for carrying out image enhancement processing on the original fingerprint image to generate a first image;
the determining module is used for determining the end point and the cross point of the fingerprint ridge line on the first image;
a knee module to determine a knee on the fingerprint ridge based on the endpoint and the cross-point;
the dimension reduction module is used for performing feature dimension reduction on the end point, the cross point and the inflection point on a second image to obtain a feature vector after dimension reduction; the second image is obtained by performing smooth denoising processing on the original fingerprint image;
and the fingerprint identification module is used for carrying out fingerprint identification based on the characteristic vector.
7. The apparatus of claim 6, wherein the image enhancement processing comprises: one or more of a gradation raising process, a gradation enhancing process, a directional filtering process, a binarization process, and an image thinning process.
8. The apparatus of claim 6, wherein the knee module is to:
if the fingerprint ridge does not contain the cross point and the length of the fingerprint ridge exceeds a preset length threshold, setting the fingerprint ridge as a ridge to be processed;
if the fingerprint ridge line comprises the cross point, dividing the fingerprint ridge line into three divided ridge lines based on the cross point; if the length of the segmentation ridge line exceeds a preset length threshold value, setting the segmentation ridge line as a ridge line to be processed;
for each ridge line to be processed, if an included angle between a first connecting line from a middle point to a first point and a second connecting line from the middle point to a second point is in a preset angle range, determining the middle point as an inflection point; the first point is a point close to an end point on one side of the ridge line to be processed; the second point is a point close to the end point on the other side of the ridge line to be processed; the distance between the first point and the close end point and the distance between the second point and the close end point are both larger than a preset distance threshold; the first point and the second point are both at the same distance from the intermediate point.
9. A terminal, characterized in that it comprises a memory in which a computer program is stored and a processor which, when executing said computer program, implements the method of fingerprint recognition according to any one of claims 1-5.
10. A storage medium, characterized in that a computer program is stored in the storage medium, which computer program, when executed, implements the method of fingerprint recognition according to any one of claims 1-5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114913555A (en) * | 2022-05-30 | 2022-08-16 | 深圳芯启航科技有限公司 | Fingerprint feature point acquisition method and device, electronic equipment and storage medium |
| CN116311389A (en) * | 2022-08-18 | 2023-06-23 | 荣耀终端有限公司 | Fingerprint identification method and device |
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2021
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114913555A (en) * | 2022-05-30 | 2022-08-16 | 深圳芯启航科技有限公司 | Fingerprint feature point acquisition method and device, electronic equipment and storage medium |
| CN116311389A (en) * | 2022-08-18 | 2023-06-23 | 荣耀终端有限公司 | Fingerprint identification method and device |
| CN116311389B (en) * | 2022-08-18 | 2023-12-12 | 荣耀终端有限公司 | Fingerprint identification method and device |
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