CN112699863A - Fingerprint enhancement algorithm, computer-readable storage medium and electronic device - Google Patents
Fingerprint enhancement algorithm, computer-readable storage medium and electronic device Download PDFInfo
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- 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
- G06V40/1347—Preprocessing; Feature extraction
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
- G06V10/507—Summing image-intensity values; Histogram projection analysis
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- 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
Abstract
The invention provides a fingerprint enhancement algorithm, which comprises the following steps: respectively performing spatial pooling on the fingerprint image to be processed according to a plurality of preset scales to obtain a plurality of groups of lattices; acquiring a gradient histogram of each grid according to the gradient values of all pixels in each grid; according to the gradient histogram of each grid, scoring the image quality of each grid to obtain the score of each grid; according to the scores of all the grids corresponding to each scale, the image quality of each group of grids is scored to obtain the score of each group of grids; according to the fraction of each group of lattices, the image quality of the fingerprint image to be processed is scored to obtain the quality fraction of the fingerprint image to be processed; and when the quality fraction of the fingerprint image to be processed reaches the enhancement standard, enhancing the fingerprint image to be processed according to a preset fingerprint enhancement algorithm to obtain an enhanced fingerprint image. The invention also provides a computer readable storage medium and an electronic device.
Description
Technical Field
The present invention relates to the field of electronic engineering, and in particular, to a fingerprint enhancement algorithm, a computer-readable storage medium, and an electronic device.
Background
With the popularization of the full-screen technology and the fingerprint identification technology in the mobile terminal, the shipment volume of the optical fingerprint sensor chip is multiplied. The optical fingerprint technology under the screen is a mainstream technology of the biological identification technical scheme of the mobile terminal equipment for a period of time in the future, and the market demand is very huge. Image quality has a large impact on the performance of fingerprint identification systems. The purpose of image enhancement is to improve overall performance by preparing the input image for subsequent processing stage optimization. Most systems extract fingerprint minutiae information from fingerprints for recognition, but inevitable noise interferes with the extraction, resulting in missing true fingerprint minutiae and possibly detecting false fingerprint minutiae, both of which negatively affect the recognition rate. To avoid these two types of errors, image enhancement aims to improve the sharpness of the ridge and valley structures. Gabor filter based techniques are widely used for fingerprint image enhancement, and the main difficulty is reliable estimation of the direction and frequency fields, i.e. the local direction and the ridge-valley frequency as input to Gabor filtering. Failure to correctly estimate results in artifacts being produced in the enhanced image, thus increasing the number of recognition or verification errors. For low quality images there is a great risk that the image enhancement step may compromise the recognition performance, even worse for very low quality images.
Therefore, how to enhance the fingerprint image and improve the definition of the ridge and valley structure of the fingerprint image is an urgent problem to be solved.
Disclosure of Invention
The invention provides a fingerprint enhancement algorithm, a computer-readable storage medium and electronic equipment, which can enhance a fingerprint image and improve the definition of ridge and valley structures of the fingerprint image so as to facilitate subsequent fingerprint identification.
In a first aspect, an embodiment of the present invention provides a fingerprint enhancement algorithm, where the method includes:
respectively performing spatial pooling on a fingerprint image to be processed according to a plurality of preset scales to obtain a plurality of groups of grids, wherein each group comprises a plurality of grids, each grid comprises a plurality of pixels, the preset scales comprise a first scale and a second scale, the first scale is larger than a preset first value, the second scale is smaller than a preset second value, and the preset first value is larger than the preset second value;
acquiring a gradient histogram of each grid according to the gradient values of all pixels in each grid;
according to the gradient histogram of each grid, scoring the image quality of each grid to obtain the score of each grid;
according to the scores of all the grids corresponding to each scale, the image quality of each group of grids is scored to obtain the score of each group of grids;
according to the fraction of each group of lattices, the image quality of the fingerprint image to be processed is scored to obtain the quality fraction of the fingerprint image to be processed;
judging whether the quality score of the fingerprint image to be processed reaches an enhancement standard or not;
and when the quality fraction of the fingerprint image to be processed reaches the enhancement standard, enhancing the fingerprint image to be processed according to a preset fingerprint enhancement algorithm to obtain an enhanced fingerprint image.
In a second aspect, embodiments of the present invention provide a computer-readable storage medium having stored thereon program instructions of the fingerprint enhancement algorithm of any one of the above items, which can be loaded and executed by a processor.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
a memory for storing program instructions for a fingerprint enhancement algorithm;
a processor for executing program instructions to cause an electronic device to implement the fingerprint enhancement algorithm of any of the above.
The fingerprint enhancement algorithm judges the quality of the fingerprint image through the gradient histogram, and avoids the false fingerprint identification caused by the enhancement of the fingerprint image with poor quality. Meanwhile, the Ateb-Gabor has richer parameters than a Gabor filter, and is more suitable for improving the definition of ridge and valley structures of the fingerprint image so as to obtain the fingerprint image which is easier to identify and improve the accuracy of fingerprint image identification.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is to be understood that the drawings in the following description are merely exemplary of the invention and that other drawings may be derived from the structure shown in the drawings by those skilled in the art without the exercise of inventive faculty.
Fig. 1 is a flowchart of a fingerprint enhancement algorithm according to a first embodiment of the present invention.
Fig. 2 is a first sub-flowchart of a fingerprint enhancement algorithm according to a first embodiment of the present invention.
Fig. 3 is a flowchart of a fingerprint enhancement algorithm according to a third embodiment of the present invention.
Fig. 4 is a second sub-flowchart of the fingerprint enhancement algorithm according to the first embodiment of the present invention.
Fig. 5 is a schematic diagram of a grid according to a first embodiment of the present invention.
Fig. 6 is a schematic view of a directional field of a grid according to a first embodiment of the present invention.
Fig. 7 is a schematic diagram of a fingerprint image to be processed and an enhanced fingerprint image according to a first embodiment of the invention.
Fig. 8 is a schematic diagram of an internal structure of an electronic device according to a first embodiment of the present invention.
Reference numerals for the various elements in the figures
| 900 | |
901 | Memory device |
| 902 | Processor with a memory having a plurality of |
903 | |
| 904 | |
905 | |
| 200 | First group of |
500 | Second group of |
| 201 | Lattices of the |
501 | Lattices of the |
| 210 | Directional field of lattice of the |
510 | Directional field of lattice of the |
| 700 | Fingerprint image to be processed | 710 | Enhanced fingerprint image |
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. 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.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Please refer to fig. 1, which is a flowchart illustrating a fingerprint enhancement algorithm according to a first embodiment of the present invention. The fingerprint enhancement algorithm provided by the first embodiment specifically includes the following steps.
And S101, respectively performing spatial pooling on the fingerprint image to be processed according to a plurality of preset scales to obtain a plurality of groups of grids. Wherein each group comprises a plurality of grids, each grid comprises a plurality of pixels, the preset plurality of scales comprise a first scale and a second scale, the first scale is larger than a preset first value, the second scale is smaller than a preset second value, and the preset first value is larger than the preset second value. The area of the lattice of the first scale is larger than that of the lattice of the second scale.
Specifically, the fingerprint image is a square fingerprint image of 160mm by 160mm, the preset first value is 70mm, and the preset second value is 40 mm. The first dimension was set to 80mm and the second dimension to 32 mm. Referring to fig. 5 in conjunction, the fingerprint image to be processed is cut into a first group of boxes 200 and a second group of boxes 500 according to two dimensions. The first set of cells is 2 x 2 cells cut according to a first scale, i.e. the first set of cells 201 cut the fingerprint image to be processed into 4 equally sized cells, and the second set of cells is 5 x 5 cells cut according to a second scale, i.e. the second set of cells 501 cut the fingerprint image to be processed into 25 cells. In the present embodiment, the fingerprint information contained in the grid obtained according to the first scale represents global fingerprint information of the entire fingerprint image, and the fingerprint information contained in the grid obtained according to the second scale represents local fingerprint information of the local fingerprint image.
In step S102, a gradient histogram of each grid is obtained from the gradient values of all pixels in each grid. Specifically, an orientation field of each grid is acquired from gradient values of a plurality of pixels in each grid, and a gradient histogram is acquired from the orientation field. Specifically, the values of the gradients in different directions in each grid are calculated, and then accumulated to obtain a gradient histogram, wherein the gradient histogram represents the image characteristics of the grid. Among them, a Histogram of Oriented Gradient (HoG) feature is a feature descriptor used for object detection in computer vision and image processing. The HoG feature constitutes a feature by calculating and counting a gradient direction histogram of a local region of the image. The HoG Ateb-Gabor filter is an image processing algorithm that uses histogram of oriented gradients and the Ateb-Gabor filter. Referring to fig. 6 in combination, the first group of lattices 201 corresponds to the first group of directional fields 210 of lattices, and the second group of lattices 501 corresponds to the second group of directional fields 510 of lattices.
And step S103, scoring the image quality of each grid according to the gradient histogram of each grid to obtain the score of each grid. Specifically, the variance between each direction and the main direction in the gradient histogram of each bin is calculated, and the score of each bin is the variance between each direction and the main direction in the gradient histogram. In this embodiment, if the variance is large, it indicates that the fingerprint texture of the lattice is obvious, the fingerprint image is easily identified by the fingerprint identification algorithm, and the fingerprint image quality is good, and if the variance is small, it indicates that the fingerprint texture of the lattice is fuzzy, the fingerprint image is not easily identified by the fingerprint identification algorithm, and the fingerprint image quality is not good. In a specific application, each grid may be scored according to the gradient histogram by using other scoring methods, for example, the range or standard deviation of the grid is calculated as the fraction of one grid according to the gradient histogram.
And step S104, scoring the image quality of each group of grids according to the scores of all grids corresponding to each scale to obtain the score of each group of grids. Specifically, the fraction of the first set of lattices is obtained by weighted addition of the fractions of 4 lattices obtained according to the first scale after averaging. In the present embodiment, the weights of the 4 lattices are all 1. The fraction of the second set of lattices is obtained by weighted addition of the fractions of the 25 lattices obtained according to the second scale after averaging. In the present embodiment, the weights of the 25 lattices are all 1. The scale and the weight are only examples and are not limited, and the corresponding scale and the weight corresponding to the fraction of each grid are set according to actual conditions in specific applications. In a specific application, each group of grids may be scored in other scoring manners, for example, a mode or a median of each group of grids is calculated as a score of each group of grids according to the score of each group of grids.
And step S105, according to the fraction of each group of grids, scoring the image quality of the fingerprint image to be processed to obtain the quality fraction of the fingerprint image to be processed. The quality score of the fingerprint image to be processed is obtained by weighted addition of the first group of lattice scores and the second group of lattice scores. In specific application, the weight corresponding to the fraction of each group of grids is set according to actual conditions. See steps S1051-S1052 for details.
In this embodiment, a multi-scale scoring mode is adopted to divide the picture into 2 × 2 and 5 × 5, so that the local score of the local fingerprint information and the global score of the global fingerprint information can be obtained simultaneously, and the quality of the fingerprint image to be processed can be evaluated more reasonably.
And step S106, judging whether the quality score of the fingerprint image to be processed reaches the enhancement standard. In this embodiment, the enhancement criterion is a criterion for determining whether the fingerprint image can be accurately recognized by the fingerprint recognition algorithm, and the enhancement criterion is 0.4 point. If the quality score of the fingerprint image to be processed is high, the quality of the fingerprint image to be processed is good.
And S107, when the quality score of the fingerprint image to be processed reaches the enhancement standard, enhancing the fingerprint image to be processed according to a preset fingerprint enhancement algorithm to obtain an enhanced fingerprint image. And when the mass fraction of the fingerprint image to be processed is more than 0.4, performing enhancement processing on the fingerprint image to be processed by utilizing a HoG and Ateb-Gabor filter to obtain an enhanced fingerprint image. See steps S1071-S1075 for details.
In the embodiment, the quality of the fingerprint image is judged through the gradient histogram, and the false fingerprint identification caused by the enhancement of the fingerprint image with poor quality is avoided. Meanwhile, the Ateb-Gabor has richer parameters than a Gabor filter, and is more suitable for improving the definition of ridge and valley structures of the fingerprint image so as to obtain the fingerprint image which is easier to identify and improve the accuracy of fingerprint image identification.
The difference between the fingerprint enhancement algorithm provided by the second embodiment and the fingerprint enhancement algorithm provided by the first embodiment is that when the quality score of the fingerprint image to be processed does not reach the enhancement standard, the fingerprint image to be processed is processed by using a preset image processing algorithm and is scored again.
In some feasible embodiments, the fingerprint enhancement algorithm counts the number of times of scoring the fingerprint image to be processed, and deletes the fingerprint image to be processed when the number of times of scoring the fingerprint image to be processed exceeds a preset value.
Please refer to fig. 2, which is a flowchart illustrating the sub-steps of step S105 according to an embodiment of the present invention. And step S105, according to the fraction of each group of grids, scoring the image quality of the fingerprint image to be processed to obtain the quality fraction of the fingerprint image to be processed. The method specifically comprises the following steps.
S1051, acquiring the weight corresponding to the fraction of each group of grids.
And S1052, calculating the quality score of the fingerprint image to be processed according to the score of each group of grids and the corresponding weight.
In some possible embodiments, in the fingerprint enhancement algorithm, the fingerprint image to be processed is a fingerprint image processed by the preprocessing step.
In other possible embodiments, the fingerprint enhancement algorithm includes a normalization process, a gaussian filtering process, and an equalization process for the fingerprint image.
Please refer to fig. 3 in combination, which is a fingerprint enhancement algorithm according to a third embodiment of the present invention. The fingerprint enhancement algorithm provided by the third embodiment is different from the fingerprint enhancement algorithm provided by the first embodiment in that the fingerprint enhancement algorithm provided by the third embodiment further comprises a preprocessing step, and the preprocessing step comprises the following steps.
S301, normalizing the fingerprint image to obtain a first fingerprint image. In this embodiment, the normalization process is performed to eliminate noise interference caused when light passes through the screen under the screen.
S302, Gaussian filtering processing is carried out on the first fingerprint image to obtain a second fingerprint image. In this embodiment, gaussian filtering is performed to further remove the interference.
And S303, carrying out equalization processing on the second fingerprint image to obtain a fingerprint image to be processed. In this embodiment, the equalization is performed to enhance the contrast of the fingerprint image.
In some possible embodiments, the fingerprint enhancement algorithm includes an Ateb-Gabor filter.
The equation for the Ateb-Gabor filter is:
wherein Ateb-G is an abbreviation of Ateb-Gabor, x is an abscissa of the pixel, y is an ordinate of the pixel, λ is a frequency of the pixel, θ is a main direction of a neighborhood in which the pixel is located,
is the phase of the pixel or pixels,
is a blurring parameter of a gaussian blur and,
is the offset of the pixel or pixels and,
is the abscissa of the change in the neighborhood of the pixel,
is the abscissa of the pixel neighborhood variation. exp is an exponential function based on the natural constant e in higher mathematics. The function Ca is the Ateb-cosine function. It has a period of
Wherein m, n are hyperparameters, m, n are positive integers, when m = n =1,
=2π。
the specific definition of (A) is as follows:
when the period is 2 pi, the Ateb-Gabor is a special form of the Gabor function, and the Ateb-Gabor function has richer parameter selection and is more suitable for fingerprint enhancement. When the Ateb-Gabor is used for fingerprint enhancement, the enhancement is obtained by counting the convolution of all pixels in the neighborhood of the current pixel (x, y) and the Ateb-Gabor kernel. The general formula for image processing by the Ateb-Gabor filter is as follows:
where x is the abscissa of the pixel, y is the ordinate of the pixel, λ is the frequency of the pixel, θ is the principal direction of the neighborhood in which the pixel is located,
is the phase of the pixel or pixels,
is a blurring parameter of a gaussian blur and,
is the offset of the pixel or pixels and,
is a row of the kernel of the filter,
is the column of the kernel of the filter,
，
is a positive integer, and i, j is a positive integer.
Please refer to fig. 4, which is a flowchart illustrating the sub-steps of step S107 according to an embodiment of the present invention. And S107, enhancing the fingerprint image to be processed according to a preset fingerprint enhancement algorithm to obtain an enhanced fingerprint image. The method specifically comprises the following steps.
S1071, all pixels on the fingerprint image to be processed are acquired.
S1072, acquiring the main direction of each pixel in the preset neighborhood according to the preset neighborhood. For each point's direction, we directly use the principal direction of the gradient in which the current point is located.
S1073, a frequency in the vertical direction of the main direction of each pixel is acquired. For each point's frequency, we directly use the inverse of the distance between two peaks in the direction perpendicular to the current point's principal direction as the frequency for fingerprint enhancement.
S1074, the main direction and the frequency of each pixel are sequentially input into an Ateb-Gabor filter to obtain the response of all the pixels.
S1075, an enhanced fingerprint image is generated from the responses of all the pixels. Referring to fig. 7 in conjunction, the pending fingerprint image 700 is enhanced to an enhanced fingerprint image 710. The ridges and valleys of the fingerprint of the enhanced fingerprint image 710 are much sharper than the pending fingerprint image 700.
In some possible embodiments, the fingerprint enhancement algorithm further includes a third scale, and the third scale is larger than the preset second value and smaller than the preset first value. Further, the plurality of dimensions may also include a fourth dimension, a fifth dimension, and so on. The preset multiple scales are determined according to actual conditions.
In this embodiment, the fingerprint image is normalized, which is mainly used to eliminate noise interference caused by passing light under the screen through the screen, perform gaussian blur to further remove the interference, and perform local histogram equalization to enhance contrast. And then, calculating gradient through a gradient histogram, scoring the quality of the picture, calculating a direction field and frequency of the picture with high score, and then performing Ateb-Gabor fingerprint enhancement on the picture with high score, wherein the original state of the picture with low quality is kept so as to avoid adding wrong fingerprint information after enhancement. The enhanced picture can be put into a classifier for further fingerprint identification.
The invention also provides a computer readable storage medium. The computer readable storage medium has stored thereon program instructions of the fingerprint enhancement algorithm described above that can be loaded and executed by a processor. In particular, the integrated unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a computer-readable storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned computer-readable storage media comprise: 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 instructions. Since the program instructions in the computer-readable storage medium include all technical solutions of all the above embodiments, at least all the advantages brought by the technical solutions of the above embodiments are achieved, and are not described herein again.
The invention also provides an electronic device 900, the electronic device 900 at least comprising a memory 901 and a processor 902. The memory 901 is used to store program instructions for the fingerprint enhancement algorithm. A processor 902 configured to execute program instructions to cause the electronic device 900 to implement the fingerprint enhancement algorithm described above. Please refer to fig. 8, which is a schematic diagram of an internal structure of an electronic apparatus 900 according to a first embodiment of the present invention.
The memory 901 includes at least one type of computer-readable storage medium, which includes flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 901 may be an internal storage unit of the electronic device 900, such as a hard disk of the electronic device 900, in some embodiments. The memory 901 may also be an external storage device of the electronic device 900 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital Card (SD), a Flash memory Card (Flash Card), etc., provided on the electronic device 900. Further, the memory 901 may also include both internal storage units and external storage devices of the electronic device 900. The memory 901 may be used to store not only application software installed in the electronic device 900 and various types of data, such as program instructions of a fingerprint enhancement algorithm, etc., but also to temporarily store data that has been output or is to be output, such as data generated by execution of a fingerprint enhancement algorithm, etc.
Processor 902 may be, in some embodiments, a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip that executes program instructions or processes data stored in memory 901. In particular, processor 902 executes program instructions of a fingerprint enhancement algorithm to control electronic device 900 to implement the fingerprint enhancement algorithm.
Further, the electronic device 900 may further include a bus 903 which may be a Peripheral Component Interconnect (PCI) standard bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
Further, electronic device 900 may also include a display component 904. The display component 904 may be an LED (Light Emitting Diode) display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light Emitting Diode) touch panel, or the like. Display component 904 may also be referred to as a display device or display unit, as appropriate, for displaying information processed in electronic device 900 and for displaying a visual user interface, among other things.
Further, the electronic device 900 may further include a communication component 905, and the communication component 905 may optionally include a wired communication component and/or a wireless communication component (e.g., a WI-FI communication component, a bluetooth communication component, etc.), which are generally used for establishing a communication connection between the electronic device 900 and other electronic devices.
While FIG. 8 illustrates only an electronic device 900 having components 901 and 905 and program instructions implementing a fingerprint enhancement algorithm, those skilled in the art will appreciate that the architecture illustrated in FIG. 8 is not limiting of the electronic device 900 and may include fewer or more components than those illustrated, or some components may be combined, or a different arrangement of components. Since the electronic device 900 adopts all technical solutions of all the embodiments described above, at least all the beneficial effects brought by the technical solutions of the embodiments described above are achieved, and are not described herein again.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above described systems, apparatuses and units may refer to the corresponding processes in the above described method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the fingerprint enhancement algorithm embodiment described above is merely illustrative, and for example, the division of the unit is only one logical functional division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, insofar as these modifications and variations of the invention fall within the scope of the claims of the invention and their equivalents, the invention is intended to include these modifications and variations.
The above-mentioned embodiments are only examples of the present invention, which should not be construed as limiting the scope of the present invention, and therefore, the present invention is not limited by the claims.
Claims (12)
1. A fingerprint enhancement algorithm, the fingerprint enhancement algorithm comprising:
respectively performing spatial pooling on a fingerprint image to be processed according to a plurality of preset scales to obtain a plurality of groups of grids, wherein each group comprises a plurality of grids, each grid comprises a plurality of pixels, the preset scales comprise a first scale and a second scale, the first scale is larger than a preset first value, the second scale is smaller than a preset second value, and the preset first value is larger than the preset second value;
acquiring a gradient histogram of each grid according to the gradient values of all pixels in each grid;
according to the gradient histogram of each grid, scoring the image quality of each grid to obtain the fraction of each grid;
according to the scores of all the grids corresponding to each scale, the image quality of each group of grids is scored to obtain the score of each group of grids;
according to the fraction of each group of lattices, the image quality of the fingerprint image to be processed is scored to obtain the quality fraction of the fingerprint image to be processed;
judging whether the quality score of the fingerprint image to be processed reaches an enhancement standard or not; and
and when the quality fraction of the fingerprint image to be processed reaches the enhancement standard, enhancing the fingerprint image to be processed according to a preset fingerprint enhancement algorithm to obtain an enhanced fingerprint image.
2. The fingerprint enhancement algorithm of claim 1, further comprising:
and when the quality score of the fingerprint image to be processed does not reach the enhancement standard, processing the fingerprint image to be processed by using a preset image processing algorithm, and scoring again.
3. The fingerprint enhancement algorithm of claim 2, wherein the number of times the fingerprint image to be processed is scored is counted, and when the number of times the fingerprint image to be processed is scored exceeds a preset value, the fingerprint image to be processed is deleted.
4. The fingerprint enhancement algorithm according to claim 1, wherein the step of scoring the image quality of the fingerprint image to be processed according to the score of each group of lattices to obtain the quality score of the fingerprint image to be processed specifically comprises:
acquiring the weight corresponding to the fraction of each group of grids;
and calculating the quality score of the fingerprint image to be processed according to the score of each group of grids and the corresponding weight.
5. The fingerprint enhancement algorithm of claim 1 wherein the fingerprint image to be processed is a fingerprint image that has been processed by the preprocessing step.
6. The fingerprint enhancement algorithm of claim 5 wherein the preprocessing step comprises normalizing, Gaussian filtering, and equalizing the fingerprint image.
7. The fingerprint enhancement algorithm of claim 6, wherein the preprocessing step specifically comprises:
normalizing the fingerprint image to obtain a first fingerprint image;
performing Gaussian filtering processing on the first fingerprint image to obtain a second fingerprint image; and
and carrying out equalization processing on the second fingerprint image to obtain the fingerprint image to be processed.
8. The fingerprint enhancement algorithm of claim 1, wherein the pre-set fingerprint enhancement algorithm comprises an Ateb-Gabor filter.
9. The fingerprint enhancement algorithm according to claim 8, wherein the enhancement processing is performed on the fingerprint image to be processed according to a preset fingerprint enhancement algorithm to obtain an enhanced fingerprint image, specifically comprising:
acquiring all pixels on the fingerprint image to be processed;
acquiring the main direction of each pixel in a preset neighborhood according to the preset neighborhood;
acquiring a frequency in a direction perpendicular to a main direction of each of the pixels;
sequentially inputting the main direction and the frequency of each pixel into the Ateb-Gabor filter to obtain the response of all the pixels; and
generating the enhanced fingerprint image according to the responses of all the pixels.
10. The fingerprint enhancement algorithm of claim 1, wherein the plurality of scales further comprises a third scale, the third scale being larger than the second predetermined value and smaller than the first predetermined value.
11. A computer readable storage medium having stored thereon program instructions of a fingerprint enhancement algorithm according to any one of claims 1 to 10, which can be loaded and executed by a processor.
12. An electronic device, characterized in that the electronic device comprises:
a memory for storing program instructions for a fingerprint enhancement algorithm; and
a processor for executing the program instructions to cause the electronic device to implement the fingerprint enhancement algorithm of any one of claims 1 to 10.
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