CN116052227B - Capacitance data processing method, system, program and device based on noise model - Google Patents
Capacitance data processing method, system, program and device based on noise model Download PDFInfo
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- 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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- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
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- G06V40/1306—Sensors therefor non-optical, e.g. ultrasonic or capacitive sensing
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Abstract
The invention discloses a capacitance data processing method, a system, a program and a device based on a noise model, which comprise the following steps: obtaining a noise reference value by using the blank graph without fingerprint data; noise reduction is carried out on the direction of the fingerprint image column according to the noise reference value; determining effective signals of the denoised fingerprint image according to gradient changes; calculating a travel signal center point according to the effective signal; and remapping based on the line signal center point to obtain a fingerprint image with better quality. The capacitance data processing method, system, program and device based on the noise model can perform noise reduction processing on the data of the capacitance fingerprint sensor to obtain a fingerprint image with clearer lines, higher contrast and better consistency compared with the fingerprint image in the prior art; furthermore, the collected fingerprint image can be applied to fingerprint identification, so that the accuracy of fingerprint identification is improved.
Description
Technical Field
The invention relates to the technical field of computer vision, in particular to a capacitance data processing method, system, program and device based on a noise model.
Background
Fingerprint identification technology refers to technology for comparing fingerprint image features of a user to be identified with fingerprint feature information in a database to verify the identity of the user. Different methods are generally required to extract fingerprint features for identification according to different features and sizes of fingerprints, and the common fingerprint features are as follows: fingerprint feature points, feature descriptors, network descriptors, etc. The fingerprint identification technology is widely applied, and the technical field of application comprises: application to mobile phones, computers, fingerprint door locks and other devices.
However, in these practical applications, the collected fingerprint images also have great differences due to the differences of the sensors, the environment, the collection modes, etc. for collecting the fingerprint images, for example: the problems of inconsistent brightness of different fingerprint images, inconsistent brightness of different areas of the same fingerprint, small effective area of the fingerprint, large noise of background areas and the like can influence the subsequent characteristic extraction and recognition flow if the processing is not performed in advance.
In the prior art, the fingerprint image processing method cannot meet the requirements of the actual application on the fingerprint image in terms of definition, contrast and consistency.
Therefore, a method is needed to improve the definition, contrast and consistency of the acquired fingerprint image, facilitate fingerprint identification, and improve the accuracy of fingerprint identification.
Disclosure of Invention
The invention aims to provide a capacitance data processing method, a system, a program and a device based on a noise model, which can improve the definition of lines in fingerprint images and the contrast of the images, so that the consistency of the images is better.
In order to solve the technical problems, the invention provides a capacitance data processing method based on a noise model, which comprises the following steps:
s1, obtaining a noise reference value by using a blank graph without fingerprint data.
S2, noise reduction is carried out on the direction of the fingerprint image column according to the noise reference value.
S3, determining effective signals of the denoised fingerprint images according to gradient changes.
And S4, calculating a travel signal center point according to the effective signal.
And S5, remapping based on the line signal center point to obtain a fingerprint image with better quality.
Further, when the noise reference value is calculated in S1, the following steps are performed:
before using the fingerprint equipment, collecting a plurality of blank pictures without fingerprint data;
and calculating the column average value minus the full-image average value of each column in the blank image without fingerprint data to obtain one row of noise distribution data.
Further, when the noise is reduced in the column direction of the fingerprint image according to the noise reference value, the corresponding noise data is subtracted in columns to remove the vertical noise.
Further, S3 distinguishes the valid signal from the invalid signal in each row of data according to the gradient threshold when the valid signal is determined according to the gradient change.
Further, S4, when calculating the line signal center point:
for an active row, the histogram in the active signal region is counted, and a weighted average is calculated as the center point of the row.
For a background and a flat row, an estimation is made with reference to the center positions of the adjacent effective rows, using the weighted values of the distribution histograms of all effective signals of the adjacent rows as the center points of the row.
Further, after calculating the line signal center points, S4 performs smoothing processing on all the center points.
Further, S5, when remapping the image: according to the central point of the line signal, mapping the whole line towards the bright and dark sides, and calculating the brightness after mapping by using a function, wherein the function is as follows:
wherein C is the brightness of the central point, G is the brightness value to be mapped, and G' is the brightness value after mapping.
In another aspect of the present invention, there is also provided a capacitive data processing system based on a noise model, comprising:
and the preprocessing module is used for obtaining a noise reference value by using the blank graph without fingerprint data.
And the noise reduction module is used for reducing noise in the direction of the fingerprint image column according to the reference value.
And the signal screening module is used for determining effective signals of the denoised fingerprint images according to gradient changes.
And the signal calculation module is used for calculating a travel signal center point according to the effective signal.
And the processing module is used for remapping based on the line signal center point to obtain a fingerprint image with better quality.
In another aspect of the invention, there is also included a computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor performs a method of capacitive data processing based on a noise model as described above.
In another aspect of the present invention, there is also provided a capacitance data processing apparatus based on a noise model, comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected; the at least one processor invokes the instructions in the memory to cause the noise model based capacitance data processing device to perform a noise model based capacitance data processing method as described above.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the capacitance data processing method based on the noise model, the noise reduction processing can be carried out on the data of the capacitance fingerprint sensor through the capacitance data processing method, the system, the program and the device based on the noise model, so that a fingerprint image with clearer lines, higher contrast and better consistency of the fingerprint image is obtained compared with the prior art;
furthermore, the collected fingerprint image can be applied to fingerprint identification, so that the accuracy of fingerprint identification is improved.
Drawings
FIG. 1 is a flow chart of a method for processing capacitance data based on a noise model according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an original image, a column-denoised image, and a final result image according to an embodiment of the present invention;
FIG. 3a is a gray level histogram of an original image according to an embodiment of the present invention;
FIG. 3b is a final gray level histogram of an embodiment of the present invention;
FIG. 4 shows a gray level histogram of two rows of data and a calculated center point according to an embodiment of the present invention;
FIG. 5 is a distribution of data centers for each row of an image in accordance with an embodiment of the present invention;
fig. 6 is a schematic diagram of a capacitive data processing device based on a noise model according to an embodiment of the invention.
Detailed Description
A method, system, program and apparatus for noise model based capacitive data processing of the present invention will be described in more detail with reference to the accompanying schematic drawings, in which preferred embodiments of the present invention are shown, it being understood that one skilled in the art may modify the invention described herein while still achieving the advantageous effects of the invention. Accordingly, the following description is to be construed as broadly known to those skilled in the art and not as limiting the invention.
The invention is more particularly described by way of example in the following paragraphs with reference to the drawings. Advantages and features of the invention will become more apparent from the following description and from the claims. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for convenience and clarity in aiding in the description of embodiments of the invention.
Capacitive fingerprint devices typically use a line-by-line scanning approach when fingerprint information is acquired. In practical applications, the resulting fingerprint image is typically noisy. The main effect on the fingerprint identification result is horizontal noise and vertical noise.
In practical development, the inventor finds that, after experiments are performed, the acquired blank graphs without fingerprint information are overlapped, and it can be obviously seen that: as images are superimposed more and more, cross grain noise becomes less and less noticeable; and the variation of the vertical streak noise is small with the superposition of the images.
As can be seen from the above, the horizontal noise is random noise, and the vertical noise is inherent noise.
The variation in the distribution of the moire noise is not too large for the same fingerprint device. Therefore, to solve the problem of vertical noise on the fingerprint image acquired by the capacitive fingerprint sensor, only the distribution of the vertical noise is recorded, and the vertical noise in the fingerprint image can be removed by using the distribution data of the vertical noise.
Correspondingly, since the cross grain noise is random noise. Therefore, to solve the problem of cross-grain noise existing in the image acquired by the capacitive fingerprint sensor, further processing of the image is required.
Based on the conclusion, the inventor provides a capacitance data processing method based on a noise model, which solves the problems of transverse noise and vertical noise in an image acquired by a capacitance type fingerprint sensor.
The invention provides a capacitance data processing method based on a noise model, please refer to fig. 1, which comprises the following steps:
s1, obtaining a noise reference value by using a blank graph without fingerprint data.
S2, noise reduction is carried out on the direction of the fingerprint image column according to the noise reference value;
s3, determining effective signals of the fingerprint image after noise reduction according to gradient changes;
s4, calculating a travel signal center point according to the effective signal;
and S5, remapping based on the line signal center point to obtain a fingerprint image with better quality.
Specifically, in S1, the noise reference value is calculated based on the blank image of the fingerprint-free image.
As an example, in calculating the noise reference value, the following steps are performed:
before using the fingerprint device, a plurality of blank pictures without fingerprint data are collected.
And subtracting the full graph mean value from the column mean value of each column in the blank graph without fingerprint data to obtain one row of noise distribution data.
Further, when noise is reduced in the column direction of the fingerprint image according to the noise reference value in S2, the corresponding noise data is subtracted by column to remove the vertical noise.
Specifically, in S3, the effective signal in each line of data is determined by gradient change.
In a specific embodiment, when determining the valid signal according to the gradient change, the valid signal and the invalid signal in each row of data are distinguished according to a gradient threshold.
In particular, in order to determine which signals in a row of fingerprints are valid signals and which signals are invalid signals, gradient information is used to determine, typically, the gradient at the edges of the fingerprint lines, i.e. at the black and white line interfaces, is large, and, in contrast, the gradient in the line interior and background area is typically small.
In summary, the effective signal region can be determined by effectively distinguishing the effective signal region according to a specific gradient threshold.
Further, in S4, the line signal center point is calculated based on the distribution of the effective signals.
Referring to fig. 4, for an active row, a histogram in the active signal region is counted, and a weighted average is calculated as the center point of the row.
For a background and a flat row, an estimation is made with reference to the center positions of the adjacent effective rows, using the weighted values of the distribution histograms of all effective signals of the adjacent rows as the center points of the row.
Specifically, for the background line and the flat line, since the center position cannot be determined by means of the effective area, it is necessary to perform estimation with reference to the center point positions of the adjacent plurality of effective lines, and then use the weighted average of the distribution histograms of all the effective signals of the adjacent lines as the center point of the line. Referring to fig. 5, fig. 5 shows the distribution of the center points of all the rows in the image, the dots in the left half are the center points calculated by the weighted average, and the center points cannot be calculated because the lower half of the image has no fingerprint signal and is a blank area, so the triangular points in the right half are estimated by the dots in the left half.
Specifically, after calculating the line signal center points, smoothing is performed on all the center points.
Since the calculation of the center points of most rows is independent of each other. Therefore, there may be some errors before and after. In summary, all the center points are smoothed to avoid too large abrupt changes.
In particular, the present invention can be applied to a capacitive fingerprint sensor.
Please refer to fig. 2 for a specific fingerprint image.
In a specific embodiment, when determining the valid signal according to the gradient change, the valid signal and the invalid signal in each row of data are distinguished according to a gradient threshold.
In particular, in order to determine which signals in a row of fingerprints are valid signals and which signals are invalid signals, gradient information is used to determine, typically, the gradient at the edges of the fingerprint lines, i.e. at the black and white line interfaces, is large, and, in contrast, the gradient in the line interior and background area is typically small.
In summary, the effective signal region can be determined by effectively distinguishing the effective signal region according to a specific gradient threshold.
In this embodiment, when calculating the line signal center point:
and further, remapping the brightness of the image based on the center point to obtain the fingerprint image with uniform brightness and high contrast after pretreatment.
In S5 of one embodiment, when remapping the image: according to the central point of the line signal, mapping the whole line towards the bright and dark sides, and calculating the brightness after mapping by using a function, wherein the function is as follows:
wherein C is the brightness of the central point, G is the brightness value to be mapped, and G' is the brightness value after mapping.
Through remapping, a map with more uniform overall fat map and more distinct bright and dark areas is obtained as shown in fig. 2.
In another aspect of the present invention, there is also included a noise model-based capacitance data processing system including:
and the preprocessing module is used for obtaining a noise reference value by using the blank graph without fingerprint data.
And the noise reduction module is used for reducing noise in the direction of the fingerprint image column according to the reference value.
And the signal screening module is used for determining effective signals of the denoised fingerprint images according to gradient changes.
And the signal calculation module is used for calculating a travel signal center point according to the effective signal.
And the processing module is used for remapping based on the line signal center point to obtain a fingerprint image with better quality.
The specific implementation manner and technical effects of the capacitive data processing system based on the noise model are similar to those of the method described in the foregoing, and are not repeated here.
Furthermore, the functions of the present invention, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a processor to carry out all or part of the steps of the method according to the embodiments of the present invention.
That is, those skilled in the art will appreciate that embodiments of the invention may be implemented in any of the forms of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects.
Based on this, the embodiment of the present invention further provides a program product, which may be a storage medium such as a usb disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, where a computer program may be stored, and when the computer program is executed by a processor, the steps of the method described in the foregoing method embodiments are performed. The specific implementation manner and the technical effect are similar, and are not repeated here.
Optionally, the embodiment of the present invention further provides a capacitance data processing device based on a noise model, where the capacitance data processing device based on the noise model may be a server, a computer, or other devices, and fig. 6 shows a schematic structural diagram of the capacitance data processing device based on the noise model provided by the embodiment of the present invention.
As shown in fig. 6, the noise model-based capacitance data processing apparatus may include: a memory 201 and at least one processor 202, said memory 201 having instructions stored therein, said memory 201 and said at least one processor 202 being interconnected;
when the noise model based capacitive data processing device is running, communication between the processor 202 and the memory 201 is via the bus 203, the processor 202 invokes said instructions in said memory 201 to execute the steps of the method as described in the previous embodiments. The specific implementation manner and the technical effect are similar, and are not repeated here.
For ease of illustration, only one processor is depicted in the above-described electronic device. It should be noted, however, that in some embodiments, an apparatus of the present invention may also include multiple processors, and thus, steps performed by one processor described in the present invention may also be performed jointly by multiple processors or separately. For example, if the processor of the apparatus performs step a and step B, it should be understood that step a and step B may also be performed by two different processors together or performed separately in one processor. For example, the first processor performs step a, the second processor performs step B, or the first processor and the second processor collectively perform steps a and B, etc.
In some embodiments, a processor may include one or more processing cores (e.g., a single core processor (S) or a multi-core processor (S)). By way of example only, the Processor may include a central processing unit (Central Processing Unit, CPU), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), special instruction set Processor (Application Specific Instruction-set Processor, ASIP), graphics processing unit (Graphics Processing Unit, GPU), physical processing unit (Physics Processing Unit, PPU), digital signal Processor (Digital Signal Processor, DSP), field programmable gate array (Field Programmable Gate Array, FPGA), programmable logic device (Programmable Logic Device, PLD), controller, microcontroller unit, reduced instruction set computer (Reduced Instruction Set Computing, RISC), microprocessor, or the like, or any combination thereof.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A method for processing capacitance data based on a noise model, comprising:
s1, obtaining a noise reference value by using a blank graph without fingerprint data;
s2, noise reduction is carried out on the direction of the fingerprint image column according to the noise reference value;
s3, determining effective signals of the fingerprint image after noise reduction according to gradient changes;
s4, calculating a row signal center point according to the effective signals, counting histograms in an effective signal area for effective rows, calculating a weighted average value as the center point of the row, and for background and flat rows, referring to the center positions of a plurality of adjacent effective rows for estimation, and using weighted values of distribution histograms of all the effective signals of the adjacent rows as the center point of the row;
s5, remapping based on the line signal center points to obtain a fingerprint image with better quality, mapping the whole line towards the bright and dark sides according to the line signal center points, and calculating by using a function to obtain the mapped brightness, wherein the function is as follows:
wherein C is the brightness of the central point, G is the brightness value to be mapped, and G' is the brightness value after mapping.
2. The noise model based capacitance data processing method as claimed in claim 1, wherein: when the noise reference value is calculated in S1, the following steps are performed:
before using the fingerprint equipment, collecting a plurality of blank pictures without fingerprint data;
and subtracting the full graph mean value from the column mean value of each column in the blank graph without fingerprint data to obtain one row of noise distribution data.
3. The method for processing capacitance data based on noise model as claimed in claim 2, wherein when S2 performs noise reduction on the column direction of the fingerprint image based on the noise reference value, the corresponding noise data is subtracted by column to remove the vertical streak noise.
4. The method of claim 1, wherein S3, when determining the effective signal according to the gradient change, distinguishes the effective signal from the ineffective signal in each line of data according to a gradient threshold.
5. The method of claim 1, wherein S4 performs smoothing on all the center points after calculating the line signal center points.
6. A capacitive data processing system based on a noise model, comprising:
the preprocessing module is used for obtaining a noise reference value by utilizing the blank graph without fingerprint data;
the noise reduction module is used for reducing noise in the direction of the fingerprint image column according to the noise reference value;
the signal screening module is used for determining effective signals of the denoised fingerprint images according to gradient changes;
the signal calculation module is used for calculating a travel signal center point according to the effective signals, counting histograms in an effective signal area for an effective row, calculating a weighted average value as the center point of the row, and for a background and a flat row, referring to the center positions of a plurality of adjacent effective rows for estimation, and using weighted values of distribution histograms of all the effective signals of the adjacent rows as the center point of the row;
the processing module is used for remapping based on the line signal center point to obtain a fingerprint image with better quality, and is used forAnd according to the central point of the line signal, mapping the whole line towards the bright and dark sides, and calculating the brightness after mapping by using a function, wherein the function is as follows:
wherein C is the brightness of the central point, G is the brightness value to be mapped, and G' is the brightness value after mapping.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the noise model based capacitance data processing method according to any of claims 1-5.
8. A capacitive data processing apparatus based on a noise model, comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected; the at least one processor invoking the instructions in the memory to cause the noise model based capacitance data processing apparatus to perform the noise model based capacitance data processing method of any of claims 1-5.
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