CN111444486B - Startup self-adaptive fingerprint parameter initialization method based on android system - Google Patents
Startup self-adaptive fingerprint parameter initialization method based on android system Download PDFInfo
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Abstract
The invention discloses a startup self-adaptive fingerprint parameter initialization method based on an android system, which comprises the following steps: step S1, calculating a noise image index; step S2, identifying the data collected by the fingerprint sensor through a preset fingerprint IC, judging whether the fingerprint sensor collects a fingerprint or a covering, if not, executing step S4; step S4, adjusting fingerprint parameters: step S4.0, determining a lower limit value A and an upper limit value B of the noise according to the noise histogram, enabling index _ max _ all to be in a [ A, B ] interval, if index _ max _ all > B, reducing the dacp value, if index _ max _ all < A, increasing the dacp value, only changing 1 for adjusting the dacp value every time, and in the adjusting process, carrying out left translation or right translation on the noise histogram; in step S4.1, the dacp when the black dot number black _ num is smaller than 12 and larger than 5 is determined as the optimum value, or the dacp when the dacp direction is adjusted more than 5 times is determined as the optimum value. The method can self-adaptively initialize the dacp value of the fingerprint parameter without using a fixed dacp value, thereby improving the compatibility and the functionality under different scenes.
Description
Technical Field
The invention relates to a fingerprint parameter processing method, in particular to a startup self-adaptive fingerprint parameter initialization method based on an android system.
Background
At present, with the increasing popularization of fingerprint technology, a plurality of android system devices are provided with fingerprint peripherals, especially mobile phones, fingerprints basically become standard configurations, the fingerprint testing is more and more perfect, the fingerprint testing is related to user safety, and the required fingerprint stability is very high.
In the prior art, the fixed use of an initialization parameter dacp value to adapt to all fingerprint sensors may have a functional problem in some situations, and the following reasons are caused:
firstly, the Dacp refers to the brightness of an image, and the value of the Dacp can be adjusted for both dry and wet fingers to obtain a better image;
secondly, the dacp value of the initialization parameter affects the sensitivity of triggering interruption of fingerprint pressing, the smaller the dacp value is, the darker the image is, when the dacp value is black to a certain extent, the gray value of the center area of the fingerprint sensor is not pressed to reach the triggering threshold value to be automatically interrupted, and when the dacp value is white to a certain extent, the gray value of the center area of the fingerprint sensor is not pressed to reach the triggering threshold value to be interrupted, so that the dacp value setting of the initialization parameter is important;
in addition, due to factors such as different fingerprint batches and process tolerances (wafer, package, module fabrication), the brightness of images of different fingerprints are different under the same initialization parameter dacp value.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a startup self-adaptive fingerprint parameter initialization method based on an android system, which can self-adaptively initialize a fingerprint parameter dacp value without using a fixed dacp value, thereby improving the compatibility and functionality under different scenes, and aiming at the defects of the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme.
A startup self-adaptive fingerprint parameter initialization method based on an android system comprises the following steps: step S1, calculating a noise image index: step S1.0, calculating the image mean value and the mean square error, calculating to obtain a histogram of the image mean value and the mean square error, using the image without 2 x 2 black edges to calculate black points black _ num, and using the image without the black edges to calculate a histogram of the image without the black edges as a local histogram; step S1.1, calculating pixel point values index _ max and index _ max _ all of the maximum values of the global histogram and the local histogram, minimum values index _ min and index _ left _ all of the pixel points in the global histogram and the local histogram, and maximum values index _ right _ all of the pixel points in the global histogram, and constructing a noise histogram according to the calculation result; step S2, identifying the data collected by the fingerprint sensor through the preset fingerprint IC, judging whether the fingerprint sensor collects the fingerprint or the covering, if yes, executing step S3, if not, executing step S4; step S3, suspending the adjustment of fingerprint parameters; step S4, adjusting fingerprint parameters: step S4.0, determining a lower limit value A and an upper limit value B of the noise according to the noise histogram, enabling index _ max _ all to be in a [ A, B ] interval, if index _ max _ all > B, reducing the dacp value, if index _ max _ all < A, increasing the dacp value, only changing 1 for adjusting the dacp value every time, and in the adjusting process, carrying out left translation or right translation on the noise histogram; in step S4.1, the dacp when the black dot number black _ num is smaller than 12 and larger than 5 is determined as the optimum value, or the dacp when the dacp direction is adjusted more than 5 times is determined as the optimum value.
Preferably, in step S1.1, after index _ min, index _ left _ all, and index _ right _ all are calculated, non-maximum suppression is performed on index _ min, index _ left _ all, and index _ right _ all.
Preferably, in step S1.1, if the difference between the pixel point values corresponding to the values in the global histogram and the local histogram that are more than 0 apart is greater than 5, the suppression processing is performed to retain the pixel points close to the middle.
Preferably, in step S1.1, the suppression processing procedure includes: for index _ min and index _ left _ all, all points to the left are removed, including their corresponding black _ num, while index _ right _ all removes all points to the right.
Preferably, in the step S2, the process of identifying the fingerprint pressing or covering includes: step S20, when the mean absolute difference is larger than the threshold 55000, the fingerprint is determined to be pressed or covered; step S21, the value of the brightness dacp of the image is adjusted to be plus or minus 1 each time, the change of 1 in the dacp value is about 10 corresponding to the change of the gray average value, and a covering object is shown when the change of the gray average value is more than 20; in step S22, if (index _ max _ all-index _ left _ all) - (index _ right _ all-index _ max _ all) <25, it is determined that there is a cover.
Preferably, the system architecture for implementing the method comprises: a first process: the fingerprint service daemon btlffpserver is started automatically and used for creating a startup self-adaptive fingerprint initialization parameter thread thBootAdjustDacpFunc and automatically adjusting a dacp parameter; and a second process: analyzing xml configuration to obtain a threshold, parameters and a switch; and a third process: and (3) writing an adjustment value into a file after the startup self-adaptive fingerprint initialization parameter dacp is successfully adjusted, reading the adjustment value from the file before the next adjustment to be used as a default dacp value, and if the file does not exist, using a code-write-dead initial value as the default value.
Preferably, resolving the thresholds, parameters and switches obtained by the xml configuration includes: parameters used in the process are adjusted, wherein the parameters comprise the time-out time adjustment BootAutDacpTimeOutCore and the maximum turning times BootAutDacpMaxTurntime in the positive and negative directions of the primary dacp; adjusting control threshold values used in the process, wherein the control threshold values comprise a maximum change value of average gray scale BootAudacpMaxMeane and a maximum value of primary average absolute difference BootAudacpMaxMeanDiff; the boot-strap adaptive fingerprint initialization parameter switch bootadadacpkey switch.
The startup self-adaptive fingerprint parameter initialization method based on the android system is realized based on the android system, the dapp parameter needs to be adjusted again when the system is started every time, the fingerprint or other covering objects cannot be pressed in the adjustment process, otherwise, the adjustment is not carried out, the adjustment is carried out after the fingerprint or the covering objects leave the fingerprint equipment, if ok is not adjusted within 60s, overtime adjustment fails, and a default value is used. Compared with the prior art, the invention can solve the problem of consistency caused by differences of ic or external conditions, has higher adjustment speed, can write the adjusted value into a file, greatly shortens the next adjustment time, and can finish adjustment within 10s generally. Meanwhile, the software of the invention has strong adaptability, uses the xml configuration switch to control whether to adjust, does not need to modify codes, and does not influence the normal use of fingerprints. Based on the principle, the fingerprint parameter dacp value can be initialized in a self-adaptive mode without using a fixed dacp value, and therefore compatibility and functionality under different scenes are improved.
Drawings
FIG. 1 is a flowchart of a method for initializing fingerprint parameters adaptively at startup according to the present invention;
FIG. 2 is a noise histogram;
fig. 3 is a diagram of a system architecture for implementing the method of the present invention.
Detailed Description
The invention is described in more detail below with reference to the figures and examples.
The invention discloses a startup self-adaptive fingerprint parameter initialization method based on an android system, please refer to fig. 1 and fig. 2, which comprises the following steps:
step S1, calculating a noise image index:
step S1.0, calculating the image mean value and the mean square error, calculating a histogram of the image mean value and the mean square error to be used as a global histogram, calculating a black point black _ num by using the image without the 2 x 2 black edge, and calculating the image histogram without the black edge to be used as a local histogram;
step S1.1, calculating pixel point values index _ max and index _ max _ all of the maximum values of the global histogram and the local histogram, minimum values index _ min and index _ left _ all of the pixel points in the global histogram and the local histogram, and maximum values index _ right _ all of the pixel points in the global histogram, and constructing a noise histogram according to the calculation result;
step S2, identifying the data collected by the fingerprint sensor through the preset fingerprint IC, judging whether the fingerprint sensor collects the fingerprint or the covering, if yes, executing step S3, if not, executing step S4;
step S3, suspending adjustment of fingerprint parameters;
step S4, adjusting fingerprint parameters:
step S4.0, determining a lower limit value A and an upper limit value B of the noise according to the noise histogram, enabling index _ max _ all to be in a [ A, B ] interval, if index _ max _ all > B, reducing the dacp value, if index _ max _ all < A, increasing the dacp value, only changing 1 for adjusting the dacp value every time, and in the adjusting process, carrying out left translation or right translation on the noise histogram;
in step S4.1, the dacp when the black dot number black _ num is smaller than 12 and larger than 5 is determined as the optimum value, or the dacp when the dacp direction is adjusted more than 5 times is determined as the optimum value.
In the method, the dacp parameter needs to be adjusted again every time the system is started based on the android system, the fingerprint or other covering objects cannot be pressed in the adjusting process, otherwise, the fingerprint or the covering objects are not adjusted, the fingerprint or the covering objects are adjusted after leaving the fingerprint equipment, if the ok is not adjusted within 60s, the overtime adjustment fails, and a default value is used. Compared with the prior art, the method can solve the problem of consistency caused by differences of ic or external conditions, has high adjustment speed, can write the adjusted value into a file, greatly shortens the adjustment time next time, and can finish adjustment within 10s generally. Meanwhile, the software of the invention has strong adaptability, uses the xml configuration switch to control whether to adjust, does not need to modify codes, and does not influence the normal use of fingerprints. Based on the principle, the fingerprint parameter dacp value can be initialized in a self-adaptive mode without using a fixed dacp value, and therefore compatibility and functionality under different scenes are improved.
In step S1.1 of this embodiment, after index _ min, index _ left _ all, and index _ right _ all are calculated, non-local maximum suppression is performed on index _ min, index _ left _ all, and index _ right _ all. In this embodiment, in step S1.1, if the difference between the pixel point values corresponding to the values more than 0 apart in the global histogram and the local histogram is greater than 5, the suppression processing is performed to keep the pixel point close to the middle.
Further, in step S1.1, the suppression processing procedure includes: for index _ min and index _ left _ all, all points to the left are removed, including their corresponding black _ num, while index _ right _ all removes all points to the right.
Preferably, the step S2 of identifying the fingerprint pressing or covering includes:
step S20, when the mean absolute difference is larger than the threshold 55000, the fingerprint is determined to be pressed or covered;
step S21, adjusting the value of the brightness dacp of the image to be positive or negative 1 each time, wherein the change of 1 in the dacp value corresponding to the gray average value is about 10, and when the change of the gray average value is more than 20, a covering is indicated;
in step S22, if (index _ max _ all-index _ left _ all) - (index _ right _ all-index _ max _ all) <25, it is determined that there is a cover.
In practical application, the startup self-adaptive fingerprint initialization parameter is used as a thread thBootAdjustDacpFanc to run when a startup fingerprint daemon service btlfserver of an android system is started, the synchronization parameter is synchronized after the thread of the thBootAdjustDacpFanc is adjusted, a fingerprint state machine normally runs, and receives an upper-layer issuing command to execute related operations, please refer to FIG. 3, and a system architecture for realizing the method comprises the following steps:
a first process: the fingerprint service daemon btlffpserver is started automatically and used for creating a startup self-adaptive fingerprint initialization parameter thread thBootAdjustDacpFunc and automatically adjusting a dacp parameter;
and a second process: analyzing xml configuration to obtain a threshold, parameters and a switch;
and a third process: and (3) writing an adjusting value into a file after the startup self-adaptive fingerprint initialization parameter dacp is successfully adjusted, reading the adjusting value from the file before the next adjustment to be used as a default value of the dacp, and if the file does not exist, using a code-write-dead initial value as the default value.
Further, analyzing the threshold, the parameter and the switch obtained by the xml configuration includes:
parameters used in the adjusting process comprise adjusting timeout time BootAudacpTimeOutCore and maximum turning times BootAudacpMaxTurntime in the positive and negative directions of a primary dacp;
adjusting control threshold values used in the process, wherein the control threshold values comprise a maximum change value of average gray scale BootAudacpMaxMean and a maximum value of primary average absolute difference BootAudacpMaxMeanDiff;
the boot-strap adaptive fingerprint initialization parameter switch bootadadacpkey switch.
The startup self-adaptive fingerprint parameter initialization method based on the android system disclosed by the invention can be realized by referring to the following embodiments after integrating the technical principles:
example one
The present embodiment may use a preset fingerprint ic having 80 × 64 pixels, and the adjustment process of the present embodiment mainly involves three aspects: and calculating a corresponding index, and pausing the adjustment when the fingerprint is pressed or the covering is covered. Referring to fig. 1, the specific steps include:
firstly, calculating a corresponding noise image index:
1. calculating the image mean value and the mean square error (for the convenience of calculation, the mean absolute difference is used for replacing), and calculating the histogram of the image mean value and the mean square error as a global histogram, because of the problems of chip packaging and the like, 2 x 2 black edges have noise influence on the calculation of certain indexes during imaging, so that the image without the 2 x 2 black edges is used for calculating the black point black _ num (non-white point 255), and the histogram of the image mean value and the mean square error is calculated as a local histogram;
2. calculating pixel point values index _ max and index _ max _ all of the maximum values of the two histograms, wherein the minimum value of pixel point values index _ min and index _ left _ all of the two histograms, and the maximum value of pixel point value index _ right _ all of the global histogram, but index _ min, index _ left _ all and index _ right _ all need to be suppressed by non-maximum values, if the pixel point values corresponding to the values of the histograms separated by more than 0 differ by more than 5, the suppression is kept on the middle side, for example, the pixel points on the left side are removed by index _ min and index _ left _ all, including the corresponding black _ num, and the pixel points on the right side are removed by index _ right _ all, as shown in fig. 2;
secondly, identifying the pressed fingerprint or the covered object:
1. through a large number of experiments, the average absolute difference is larger than the threshold 55000, so that the fingerprint can be considered as pressed or covered, the image is indicated to have variance change and obvious image information, and the image can not be identified when safe pressing or edge pressing is less;
2. the value of the brightness degree dacp of the image is adjusted to be plus or minus 1 each time, the change of 1 of the dacp value corresponding to the change of the average gray level value is about 10 after a large amount of experiments, and if the change of the covering object exists, the change is greater than 20, so that the covering object is considered to exist if the change of the average gray level value before and after the dacp is adjusted each time is greater than 20, but the covering object cannot be distinguished if the covering object exists before the adjustment;
3. it was proven from a number of experiments that if (index _ max _ all-index _ left _ all) - (index _ right _ all-index _ max _ all) <25, then there is a cover, since index _ max _ all-index _ left _ all is much larger than index _ right _ all-index _ max _ all when there is no cover;
thirdly, adjusting the two-step process:
1. the first step of the adjustment is to make index _ max _ all in the [130,210] interval, because if the noise in this interval is quantifiable, it is better to determine whether the index is covered, then the 5 th judgment can be performed to determine whether there is covered, as the two vertical dotted lines of the noise histogram are 130 and 210, if >210, the dacp value is decreased, and <130, the dacp value is increased, and the value of each adjustment of dacp is changed by only 1, for example, the noise histogram is shifted left or right until the condition is satisfied;
2. if the black point number black _ num is smaller than 12 and larger than 5, the dacp is considered to be the optimal value at the moment, in order to improve the compatibility of different fingerprints, the situation that the optimal value cannot be reached may exist, so that the optimal value is considered when the dacp direction is turned over (adjusted) for more than 5 times, the noise just saturates right overflow, the problem that the grey value of the central area of the fingerprint sensor is not pressed to reach the trigger threshold value and is automatically interrupted or the grey value of the central area of the fingerprint sensor is too large and the noise overflows left too much is caused to not reach the trigger threshold value and is not interrupted is achieved, and the purpose is achieved.
Referring to fig. 3, the startup self-adaptive fingerprint initialization parameter is used as a thread thBootAdjustDacpFunc to run when the startup fingerprint daemon service btlfppserver of the android system is started, the thread of the thBootAdjustDacpFunc is adjusted to synchronize the parameter, the fingerprint state machine normally runs, receives an upper layer issued command to execute related operations, and the total of the startup self-adaptive fingerprint initialization parameter is composed of three parts:
firstly, a fingerprint service daemon btlfpserver is started up automatically, a startup self-adaptive fingerprint initialization parameter thread thBootAdjustDacpFunc is established, and a dapp parameter is automatically adjusted;
analyzing xml configuration to obtain a threshold value, parameters and a switch, wherein the method mainly comprises the following three parts:
1. adjusting parameters used in the process, such as adjusting timeout time BootAuutoDacpTimeoutCore, maximum turnover times of the dacp in the positive and negative directions BootAuutoDacpMax time and the like;
2. adjusting control threshold values used in the process, such as a maximum change value of average gray scale BootAuutoDacpMaxMean, a maximum value of average absolute difference BootAuutoDacpMaxMeanDiff and the like;
3. a startup self-adaptive fingerprint initialization parameter switch BootAuutoDacpKEySwitch;
and thirdly, after the startup self-adaptive fingerprint initialization parameter dacp is successfully adjusted, an adjustment value is written into the file, the value is read from the file before the next adjustment to be used as a default dacp value, and if the file does not exist, an initial value of code deadlock is used as a default value.
In conclusion, the invention can enable all fingerprint sensors to have the self-adaptive initialization parameter dacp value, and no fixed dacp value is used, thereby solving the functional problem caused by compatibility under different scenes and better meeting the application requirement.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents or improvements made within the technical scope of the present invention should be included in the scope of the present invention.
Claims (7)
1. A startup self-adaptive fingerprint parameter initialization method based on an android system is characterized by comprising the following steps:
step S1, calculating a noise image index:
step S1.0, calculating the image mean value and the mean square error, calculating a histogram of the image mean value and the mean square error to be used as a global histogram, calculating the black point number black _ num by using the image without 2 x 2 black edges, and calculating the image histogram without the black edges to be used as a local histogram;
step S1.1, calculating a maximum value index _ max of a pixel point in a global histogram and a maximum value index _ max _ all of a pixel point in a local histogram, a minimum value index _ min of a pixel point in the global histogram and a minimum value index _ left _ all of a pixel point in the local histogram, and a maximum value index _ right _ all of a pixel point in the global histogram, and constructing a noise histogram according to a calculation result;
step S2, identifying the data collected by the fingerprint sensor through the preset fingerprint IC, judging whether the fingerprint sensor collects the fingerprint or the covering, if yes, executing step S3, if not, executing step S4;
step S3, suspending adjustment of fingerprint parameters;
step S4, adjusting fingerprint parameters:
step S4.0, determining a lower limit value A and an upper limit value B of noise according to the noise histogram, and enabling index _ max _ all to be in an interval of [ A, B ], if index _ max _ all > B, reducing the image brightness degree value, if index _ max _ all < A, increasing the image brightness degree value, only changing 1 for adjusting the image brightness degree value each time, and in the adjusting process, carrying out left translation or right translation on the noise histogram;
in step S4.1, the image brightness value when the black dot number black _ num is smaller than 12 and larger than 5 is regarded as the optimum value, or the image brightness value when the direction of the adjusted image brightness value exceeds 5 times is regarded as the optimum value.
2. The method as claimed in claim 1, wherein in step S1.1, after index _ min, index _ left _ all and index _ right _ all are calculated, non-maximum suppression is performed on index _ min, index _ left _ all and index _ right _ all.
3. The android system-based boot-strap adaptive fingerprint parameter initialization method of claim 2, wherein in step S1.1, if a difference between pixel point values corresponding to values in the global histogram and the local histogram with a separation greater than 0 is greater than 5, then a pixel point close to the middle is retained for suppression processing.
4. The android system-based boot-strap adaptive fingerprint parameter initialization method of claim 3, wherein in step S1.1, the suppression processing procedure includes: for index _ min and index _ left _ all, all points to the left are removed, including their corresponding black _ num, while index _ right _ all removes all points to the right.
5. The android system-based power-on adaptive fingerprint parameter initialization method of claim 4, wherein in the step S2, the process of identifying a fingerprint pressing or covering includes:
step S20, when the mean absolute difference is larger than the threshold 55000, the fingerprint is determined to be pressed or covered;
step S21, adjusting the brightness value of the image to be positive or negative 1 each time, and the change of the brightness value 1 corresponding to the change of the gray average value is 10, when the change of the gray average value is more than 20, the covering is indicated;
in step S22, if (index _ max _ all-index _ left _ all) - (index _ right _ all-index _ max _ all) <25, it is determined that there is a cover.
6. The android system-based boot-strap adaptive fingerprint parameter initialization method of claim 5, wherein a system architecture for implementing the method comprises:
a first process: the fingerprint service daemon btlffpserver is started automatically and is used for creating a starting self-adaptive fingerprint initialization parameter thread thBootAdjust image brightness value Func and automatically adjusting an image brightness value parameter;
and a second process: analyzing xml configuration to obtain a threshold, parameters and a switch;
and a third process: the method comprises the steps that a starting-up self-adaptive fingerprint initialization parameter image brightness and darkness value is written into a file after adjustment is successful, the adjustment value is read from the file before the next adjustment and is used as an image brightness and darkness value default value, and if the file does not exist, an initial value defined in a code is used as the default value.
7. The android system-based power-on adaptive initialization fingerprint parameter method of claim 6, wherein analyzing the thresholds, parameters and switches obtained by xml configuration comprises:
parameters used in the adjusting process comprise an adjusting timeout BootAuto image brightness value TimeoutCore and a BootAuto image brightness value MaxTurntime of the maximum turning times in the positive and negative directions of the primary image brightness value;
adjusting control thresholds used in the process, wherein the control thresholds comprise a maximum variation value of average gray scale BootAuto image brightness value MaxMean and a maximum value of primary average absolute difference BootAuto image brightness value MaxMeanDiff;
and starting the self-adaptive fingerprint initialization parameter switch BootAuto image brightness value KeySwitch.
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