WO2016112737A1 - 指纹传感器及其校正方法 - Google Patents

指纹传感器及其校正方法 Download PDF

Info

Publication number
WO2016112737A1
WO2016112737A1 PCT/CN2015/094441 CN2015094441W WO2016112737A1 WO 2016112737 A1 WO2016112737 A1 WO 2016112737A1 CN 2015094441 W CN2015094441 W CN 2015094441W WO 2016112737 A1 WO2016112737 A1 WO 2016112737A1
Authority
WO
WIPO (PCT)
Prior art keywords
correction
sensing unit
new
correction coefficient
old
Prior art date
Application number
PCT/CN2015/094441
Other languages
English (en)
French (fr)
Inventor
庞树
黄辉
蔡小淳
Original Assignee
深圳市汇顶科技股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳市汇顶科技股份有限公司 filed Critical 深圳市汇顶科技股份有限公司
Priority to EP15877648.4A priority Critical patent/EP3182329B1/en
Priority to KR1020177007213A priority patent/KR101875349B1/ko
Publication of WO2016112737A1 publication Critical patent/WO2016112737A1/zh
Priority to US15/459,575 priority patent/US10643048B2/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/13—Sensors therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1347—Preprocessing; Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G06V10/993—Evaluation of the quality of the acquired pattern
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/13—Sensors therefor
    • G06V40/1306—Sensors therefor non-optical, e.g. ultrasonic or capacitive sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1347—Preprocessing; Feature extraction
    • G06V40/1359—Extracting features related to ridge properties; Determining the fingerprint type, e.g. whorl or loop
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification

Definitions

  • the present invention relates to the field of fingerprint recognition technologies, and in particular, to a fingerprint sensor and a calibration method thereof.
  • Fingerprint is the texture formed by uneven skin on the surface of the finger.
  • the texture feature of the fingerprint is unique and stable, so it is often used as the basis for identification.
  • a fingerprint sensor is a sensor that uses fingerprints to identify an identity.
  • the fingerprint sensor includes a fingerprint sensing chip 10 and a protective cover 20 (Cover) fixed to the surface of the fingerprint sensing chip 10 by an adhesive 30 for protecting the fingerprint sensing chip 10.
  • the fingerprint sensing chip 10 has a plurality of sensing units 11 (Pixels). As shown in FIG. 2, the plurality of sensing units 11 are arranged in a matrix.
  • the sensing unit 11 is configured to sense fingerprint signals and output data, and the fingerprint signals are fingerprint textures. Depth information.
  • the surface of the protective cover 20 serves as a sensing surface of the fingerprint sensor. When the finger presses the sensing surface, the matrix composed of the depth information of the fingerprint texture output by all the sensing units 11 is the texture information of the finger.
  • the difference between the different sensing units 11 in the same fingerprint sensor, the unevenness of the adhesive 30 and its impurities, the flatness of the protective cover 20, etc. may cause the response between the sensing units 11 not to be Uniform, so the accurate fingerprint texture features cannot be directly obtained through the data output by the fingerprint sensor, and the output data must be corrected to eliminate this non-uniformity.
  • the prior art practice is to perform a calibration operation on the fingerprint sensor before leaving the factory, and the correction operation uses a two-point calibration method.
  • the method is based on the linear relationship between the data output by the sensing unit 11 and the input signal of the sensing unit 11 (ie, the depth information of the fingerprint texture), and the data output by the sensing unit 11 is y, and the signal input by the sensing unit 11 is x.
  • the specific process of the two-point calibration method is: first, when the fingerprint sensor is unloaded (that is, when there is no object pressing on the sensing surface of the sensor), one frame of data output by the sensing unit 11 is obtained, and then the fingerprint sensor is acquired.
  • One frame of data output by the sensing unit 11 when a flat metal block 50 is placed thereon (as shown in FIG. 4).
  • the following equations are respectively established for each sensing unit 11 by the above two frames of data:
  • P1(x 1 , y 1 ) is the data output at no load
  • P2(x 2 , y 2 ) is the data output when the metal block is placed
  • the output data can be corrected by the following formula:
  • N and M are the number of rows and columns of the sensing unit 11 in the fingerprint sensor, respectively, y is the data output by the sensing unit 11, and x is the corrected data (ie, the input signal of the sensing unit 11).
  • the main object of the present invention is to provide a fingerprint sensor and a calibration method thereof, which aim to correct the fingerprint sensor during use and automatically correct the correction coefficient to reduce the probability of product repair and prolong the service life of the product.
  • the present invention provides a fingerprint sensor having a sensing surface for finger contact, including a plurality of sensing units, a processing unit, and a correction unit.
  • a sensing unit configured to sense a fingerprint signal and output the first data when starting the calibration
  • a processing unit configured to calculate, according to the mathematical model, a new correction coefficient of the corresponding sensing unit by using the first data
  • a correcting unit configured to correct the second data output by the corresponding sensing unit according to the new correction coefficient.
  • the invention also provides a method for correcting a fingerprint sensor, the fingerprint sensor having a sensing surface for contacting a finger, the sensing surface comprising a plurality of sensing units, comprising the steps of:
  • the fingerprint sensor provided by the invention obtains the correction signal by sensing the fingerprint signal generated by the actual finger pressing and acquiring the data output by the sensing unit during use, so that the correction operation can be performed at any time during use, when the fingerprint sensor When the physical characteristics change, the correction coefficient can be automatically corrected, thereby reducing the probability of product repair and prolonging the service life of the product. Moreover, the fingerprint sensor of the embodiment of the invention simplifies or eliminates the calibration link before leaving the factory, thereby reducing production difficulty and production cost.
  • FIG. 1 is a schematic structural view of a fingerprint sensor
  • FIG. 2 is a schematic view showing the arrangement of the sensing unit
  • 3 is a schematic diagram showing the relationship between the data output by the sensing unit and the input signal
  • FIG. 5 is a block diagram of a fingerprint sensor of the present invention.
  • FIG. 6 is a schematic block diagram of a first embodiment of a fingerprint sensor of the present invention.
  • FIG. 7 is a schematic diagram of a finger pressing fingerprint sensor in a first embodiment of the fingerprint sensor of the present invention.
  • FIG. 8 is a schematic diagram of a finger sensor pressing a fingerprint sensor multiple times in a first embodiment of the fingerprint sensor of the present invention
  • FIG. 9 is a schematic block diagram of a second embodiment of a fingerprint sensor of the present invention.
  • Figure 10 is a block diagram showing a third embodiment of the fingerprint sensor of the present invention.
  • FIG. 11 is a flow chart showing a first embodiment of a method for correcting a fingerprint sensor of the present invention
  • FIG. 12 is a flow chart of a second embodiment of a method for correcting a fingerprint sensor of the present invention.
  • Figure 13 is a flow chart showing a third embodiment of the method of correcting the fingerprint sensor of the present invention.
  • the fingerprint sensor has a sensing surface for finger pressing, and the sensing surface usually covers the protective cover as shown in FIG.
  • the sensing surface includes a plurality of sensing units arranged in an array, a processing unit and a correction unit.
  • Sensing unit used to sense the fingerprint signal and output the first data when starting the calibration.
  • Processing unit configured to calculate a new correction coefficient of the corresponding sensing unit by using the first data according to the mathematical model.
  • the fingerprint sensor has two modes of a normal mode and a correction mode.
  • the normal mode is used for normal fingerprint sensing and outputting sensing data
  • the correction mode is used to correct the fingerprint sensor to obtain a correction coefficient.
  • the fingerprint sensor can perform mode switching through a mode switching unit.
  • Correction unit for correcting the second data output by the corresponding sensing unit according to the new correction coefficient.
  • the sensing unit is configured to acquire one frame of data output by the fingerprint sensor when it is idling, and is also used to acquire at least two outputs of the fingerprint sensor when the finger is pressed at different positions of the sensing surface. Frame data and send it to the processing unit.
  • the sensing unit senses the fingerprint signal and acquires the first data outputted when the one-frame fingerprint sensor is unloaded (ie, when the sensing surface is not pressed by any object).
  • the first data output by all sensing units is one frame of data.
  • the user is prompted to press the different positions of the sensing surface with the finger.
  • the data output by the one-frame sensing unit is acquired, that is, how many times the finger presses the sensing surface, how much frame data is acquired.
  • the data output by the sensing unit is the maximum value when pressed onto the sensing unit; and when the fingerprint groove portion (ie, the depressed portion of the fingerprint) is pressed to When the surface is sensed, the data output by the corresponding sensing unit is a small value.
  • the sensing units 11b, 11h, 11m, 11n, 11s will output a maximum value, while the other sensing units will output a smaller value.
  • each sensing unit on the fingerprint sensor can be pressed by the fingerprint ridge.
  • the number of times the finger presses the sensing surface that is, the number of frames acquired when the finger is not empty, according to actual needs.
  • the larger the sensing surface is the more frames are required to be acquired.
  • the smaller the sensing surface is the smaller the number of frames to be acquired is.
  • all the sensing units are pressed by the finger ridges. For example, as shown in FIG. 8, each sensing unit has been pressed by the fingerprint ridge after being pressed 5 times by the finger, and the sensing unit acquires 5 frames of output data correspondingly.
  • the processing unit includes a selection subunit and a second calculation subunit, and the selection subunit is configured to select a maximum value corresponding to each sensing unit from the at least two frames of data.
  • the second calculation subunit is used to calculate a new correction coefficient of the sensing unit.
  • the selected subunit selects a maximum value corresponding to each sensing unit from at least two frames of data acquired when the device is not idling, that is, data corresponding to output when all the sensing units of one frame are pressed by the fingerprint ridge.
  • This frame data is similar to the data output by the sensing unit when the flat metal block is pressed against the sensing surface when the correction is performed in the prior art, and the frame data and the data outputted by the sensing unit of one frame at a time can be corrected once.
  • the data acquired by one sensing unit when idle is Dmin
  • the maximum value of the data acquired by the sensing unit is Dmax when non-empty
  • the finger pressing the sensing surface Num times (Num is constant, depending on the specific situation), multiple inductions
  • Dnum i,j represents the ith row
  • the data of the Num frame output by the sensing unit of the j column i ⁇ [1, N], j ⁇ [1, M].
  • the storage unit stores a correction coefficient, replaces the correction coefficient obtained by the current correction with the old correction coefficient as a new correction coefficient, and stores the new correction coefficient.
  • the old correction coefficient and the correction coefficient obtained by the current correction are weighted and replaced, and the old correction coefficient is replaced and stored.
  • the correction coefficients obtained by this correction be k new and b new
  • the old correction coefficients are k old and b old
  • the new correction coefficients are k and b
  • the weights are 0.8 and 0.2
  • the weights can be set according to specific conditions. , there are:
  • the replacement method of the weighted summation of the old and new correction coefficients can speed up the convergence speed of the correction coefficient calculation and improve the robustness of the system.
  • this correction is the first correction, and the correction coefficient obtained this time is used as the new correction coefficient of the fingerprint sensor and stored.
  • the correction unit corrects the data output by the sensing unit according to the following formula:
  • N and M are the number of rows and columns of the sensing unit in the fingerprint sensor respectively, y' is the second data output by the sensing unit, and x is the data corrected by the new correction coefficient.
  • the fingerprint sensor in the first embodiment of the present invention obtains the data output by the sensing unit when at least two frames of the finger press the different positions of the sensing surface, and selects the maximum value therefrom, thereby forming all the sensing units of one frame are pressed by the fingerprint ridge.
  • the output data which is similar to the data output by the sensing unit when the flat metal block is pressed against the sensing surface when the correction is performed in the prior art, and the data output by the sensing unit can be used with the frame data and a frame of no-load sensing unit.
  • the fingerprint sensor is corrected once to obtain a correction factor.
  • the fingerprint sensor of the embodiment of the invention can perform the correction operation at any time during use, and can automatically correct the correction coefficient when the physical characteristics of the fingerprint sensor change, thereby achieving the effect of reducing the product repair probability and prolonging the service life of the product. Moreover, the fingerprint sensor of the embodiment of the invention simplifies or eliminates the calibration link before leaving the factory, thereby reducing production difficulty and production cost.
  • the processing unit includes a first calculation subunit for using the first data output by the sensing unit and the corresponding old correction according to the mathematical model. Coefficient, calculate the new correction factor.
  • k new and b new are the new correction coefficients of the sensing unit
  • k old And b old is the old correction coefficient of the sensing unit
  • y is the first data output by the sensing unit
  • Y is the image composed of the first data output by all the sensing units
  • F(Y) is the median filtering of Y.
  • the data of the sensing unit Avg(Y) is the data of the sensing unit corresponding to the average of Y, and ⁇ is the learning factor, and the value ranges from [0, 1].
  • x old is the data obtained after the sensing unit is corrected by the old correction coefficient.
  • the correction unit corrects the data output by the sensing unit according to the following formula:
  • N and M are the number of rows and columns of the sensing unit in the fingerprint sensor respectively, y' is the second data output by the sensing unit, and x is the data corrected by the new correction coefficient.
  • a frame of first data is obtained by one finger pressing, and a new correction coefficient is calculated by combining the old correction coefficient.
  • the fingerprint sensor of the embodiment of the invention can perform the correction operation at any time during use, and can automatically correct the correction coefficient when the physical characteristics of the fingerprint sensor change, thereby achieving the effect of reducing the product repair probability and prolonging the service life of the product.
  • a difference from the first embodiment and the second embodiment is that a judging unit is added.
  • the judging unit is configured to judge whether the correction condition is satisfied, and when it is determined that the correction condition is satisfied, the correction mode is turned on, and the correction operation is started.
  • the correction conditions include, but are not limited to, one of the first time the fingerprint sensor is enabled, the preset correction time is reached, the correction command is received, and the effect of the old correction coefficient correction does not meet the specified requirements.
  • the judging unit detects that the fingerprint sensor is first activated, it determines that the correction condition is satisfied, and turns on the correction mode.
  • the correction time may be set, and when the determination unit detects that the preset correction time is reached, it is determined that the correction condition is satisfied, and the correction mode is turned on.
  • the correction time may be a specific time, such as setting a correction at a certain time on a certain day of the month; or an interval time, such as setting the calibration every three months.
  • the effect of the old correction coefficient correction may be evaluated, and when the determination unit determines that the effect does not reach the specified requirement, the correction condition is satisfied, and the correction mode is turned on.
  • the effect of the old correction coefficient correction is evaluated by the root mean square error or image smoothness of all the sensing unit output data corrected by the old correction coefficient.
  • N and M are the number of rows and columns of the sensing unit in the fingerprint sensor
  • y is the data output by the sensing unit
  • x old is the data corrected by the sensing unit after the old correction coefficient
  • f is the corrected data of all the sensing units.
  • Image, h 1 [1, -1].
  • the user can also manually enable the calibration mode.
  • the user issues a correction command through a menu option, a function or a virtual button, a touch command, a preset gesture action, etc., and after receiving the correction command, the determination unit determines that the correction condition is satisfied and turns on the correction mode.
  • the determining unit may also determine whether the correction condition is satisfied by determining the current mode, and if it is detected that the correction mode is currently present, it is determined that the correction condition is satisfied.
  • the fingerprint sensor has a sensing surface for finger contact, and the sensing surface includes a plurality of sensing units.
  • the calibration method includes the following steps:
  • Step S100 Switch to the correction mode.
  • the fingerprint sensor has two modes of a normal mode and a correction mode.
  • the normal mode is used for normal fingerprint sensing and outputting sensing data
  • the correction mode is used to correct the fingerprint sensor to obtain a correction coefficient.
  • the fingerprint sensor is switched to the correction mode, the correction operation is started.
  • Step S110 Acquire one frame of data output by the sensing unit of the fingerprint sensor during no-load.
  • the fingerprint sensor has a sensing surface for finger pressing, and the sensing surface is usually covered with a protective cover as shown in FIG.
  • the fingerprint sensor includes a plurality of sensing units arranged in an array for sensing fingerprint signals and outputting data. After the calibration is started, the fingerprint sensor automatically acquires one frame of data output by all the sensing units when the fingerprint sensor is unloaded (ie, when the sensing surface is not pressed by any object).
  • Step S120 Acquire at least two frames of data output by the sensing unit when the fingerprint sensor presses different positions of the sensing surface.
  • the fingerprint sensor can prompt the user to press the different positions of the sensing surface with the finger.
  • the data output by all the sensing units of one frame is acquired, that is, the finger pressing the sensing surface How many times, how many frames of second data are acquired.
  • the position and angle of the user's finger pressing the sensing surface are different each time, so when the user presses the fingerprint sensor with the finger multiple times, each sensing unit on the fingerprint sensor can be pressed by the fingerprint ridge. .
  • the number of times the finger presses the sensing surface that is, the number of frames to obtain data, which is determined according to actual needs. Generally, the larger the sensing surface is, the more frames are required to be acquired. The smaller the sensing surface is, the smaller the number of frames to be acquired is.
  • the sensing unit is pressed by the finger ridge. For example, as shown in FIG. 8, each sensing unit has been pressed by the fingerprint ridge after being pressed 5 times by the finger, and the data output by the 5-frame sensing unit is correspondingly acquired.
  • Step S130 Select a maximum value corresponding to each sensing unit from the at least two frames of data.
  • the fingerprint sensor selects a maximum value from at least two frames of data output by each sensing unit, which is equivalent to data outputted when all the sensing units of one frame are pressed by the fingerprint ridge, and the frame data is performed in the prior art.
  • the data output from the sensing unit is approximated by pressing the sensing surface with the flat metal block, and the correction can be performed by using the frame data and the data of one frame at no load.
  • Dmin the maximum value when the finger is pressed
  • Dmax the finger presses the sensing surface Num times (Num is constant, depending on the specific situation)
  • the plurality of sensing units are arranged in N rows and M columns
  • Dmax i,j MAX(D1 i,j , D2 i,j , D3 i,j ,...,Dnum i,j ), where Dnum i,j represents the data of the Num frame output by the i-th row and the j-th column sensing unit , i ⁇ [1,N],j ⁇ [1,M].
  • Step S150 It is judged whether there is an old correction coefficient.
  • step S160 If the fingerprint sensor has been previously calibrated or has been initially corrected at the factory, and the old correction coefficient is stored, the process proceeds to step S160; if the fingerprint sensor has not been corrected before, the current correction is the first correction, and there is no old correction coefficient. Then, the process proceeds to step S170.
  • Step S160 The correction coefficient obtained by the current correction is replaced with the old correction coefficient as a new correction coefficient.
  • the fingerprint sensor When there is an old correction coefficient, the fingerprint sensor replaces the correction coefficient obtained by the current correction with the old correction coefficient as a new correction coefficient, and stores the new correction coefficient.
  • the new correction coefficient and the old correction coefficient may also be weighted and summed, and the calculation result is replaced with the old correction coefficient as a new correction coefficient and stored.
  • the correction coefficients obtained by this correction be k new and b new
  • the old correction coefficients are k old and b old
  • the new correction coefficients are k and b
  • the weights are 0.8 and 0.2
  • the weights can be set according to specific conditions. , there are:
  • the replacement method of the weighted summation of the old and new correction coefficients can speed up the convergence speed of the correction coefficient calculation and improve the robustness of the system.
  • Step S170 directly use the correction coefficient obtained by the current correction as the correction coefficient of the fingerprint sensor.
  • the fingerprint sensor directly uses the correction coefficient obtained this time as the correction coefficient of the fingerprint sensor and stores it.
  • N and M are the number of rows and columns of the sensing unit in the fingerprint sensor respectively, y' is the second data output by the sensing unit, and x is the data corrected by the new correction coefficient.
  • the data at the time of no-load acquisition may be performed after step S110 is performed after the maximum value is selected in step S130.
  • the data output by the sensing unit when the finger touches the different positions of the sensing surface is obtained, and the maximum value is selected therefrom, thereby composing data outputted when all the sensing units of one frame are pressed by the fingerprint ridge.
  • the frame data is the same as the data output by the sensing unit when the flat metal block is pressed by the sensing unit when the correction is performed in the prior art, and the fingerprint sensor can be corrected once by using the frame data and the data output by the sensing unit of one frame at no load. Obtain the correction factor.
  • the fingerprint sensor of the embodiment of the invention can perform the correction operation at any time during use, and can automatically correct the correction coefficient when the characteristics of the fingerprint sensor change, thereby achieving the effect of reducing the product repair probability and prolonging the normal service life of the product. Moreover, the fingerprint sensor of the embodiment of the invention simplifies or eliminates the calibration link before leaving the factory, thereby reducing production difficulty and production cost.
  • the calibration method includes the following steps:
  • Step S200 Switch to the correction mode.
  • Step S210 When the calibration is started, the fingerprint signal is sensed, and the first data output by each sensing unit is acquired.
  • Step S220 Calculate a new correction coefficient of the corresponding sensing unit by using the first data and the old correction coefficient of the corresponding sensing unit according to the mathematical model.
  • k new and b new are the new correction coefficients of the sensing unit
  • k old And b old is the old correction coefficient of the sensing unit
  • y is the first data output by the sensing unit
  • Y is the image composed of the first data output by all the sensing units
  • F(Y) is the median filtering of Y.
  • the data of the sensing unit Avg(Y) is the data of the sensing unit corresponding to the average of Y, and ⁇ is the learning factor, and the value ranges from [0, 1].
  • x old is the data obtained after the sensing unit is corrected by the old correction coefficient.
  • Step S230 It is judged whether there is an old correction coefficient.
  • step S240 If the fingerprint sensor has been previously corrected or has been initially corrected at the factory, and the old correction coefficient is stored, the process proceeds to step S240; if the fingerprint sensor has not been corrected before, the current correction is the first correction, and there is no old correction coefficient. Then, the process proceeds to step S250.
  • Step S240 The correction coefficient obtained by the current correction is replaced with the old correction coefficient as a new correction coefficient.
  • the fingerprint sensor When there is an old correction coefficient, the fingerprint sensor replaces the correction coefficient obtained by the current correction with the old correction coefficient as a new correction coefficient, and stores the new correction coefficient.
  • the new correction coefficient and the old correction coefficient may also be weighted and summed, and the calculation result is replaced with the old correction coefficient as a new correction coefficient and stored.
  • the correction coefficients obtained by this correction be k new and b new
  • the old correction coefficients are k old and b old
  • the new correction coefficients are k and b
  • the weights are 0.8 and 0.2
  • the weights can be set according to specific conditions. , there are:
  • the replacement method of the weighted summation of the old and new correction coefficients can speed up the convergence speed of the correction coefficient calculation and improve the robustness of the system.
  • Step S250 directly use the correction coefficient obtained by the current correction as the correction coefficient of the fingerprint sensor.
  • Step S260 Correct the second data output by the corresponding sensing unit according to the new correction coefficient.
  • the sensing unit is according to the following formula:
  • the output data is corrected:
  • N and M are the number of rows and columns of the sensing unit in the fingerprint sensor respectively, y' is the second data output by the sensing unit, and x is the data corrected by the new correction coefficient.
  • Steps S230, S240, and S250 are in one-to-one correspondence with steps S150, S160, and S170, and are not described herein.
  • a frame of first data is obtained by one finger pressing, and a new correction coefficient is calculated by combining the old correction coefficients.
  • the fingerprint sensor of the embodiment of the invention can perform the correction operation at any time during use, and can automatically correct the correction coefficient when the physical characteristics of the fingerprint sensor change, thereby achieving the effect of reducing the product repair probability and prolonging the service life of the product.
  • the calibration method includes the following steps:
  • Step S300 It is judged whether the correction condition is satisfied.
  • This embodiment enables the correction mode by setting a correction condition.
  • the fingerprint sensor When it is detected that the fingerprint sensor is first enabled, or detects that the preset correction time is reached, or the correction instruction is received, or the effect of the old correction coefficient correction does not reach the specified requirement, it is determined that the correction condition is satisfied, the correction mode is turned on, and the In step S310, the correcting operation is started. When it is determined that the correction condition is not satisfied, the detection judgment is continued.
  • the default on correction mode can be set.
  • the calibration time may be set, and the calibration time may be a specific time, such as setting a certain time on a certain day of the month to perform correction; or an interval time, such as setting the calibration every three days. It is also possible that the user manually activates the calibration mode by issuing a correction command through menu options, functions or virtual buttons, touch commands, preset gesture actions, and the like.
  • the effect of the old correction coefficient correction may be evaluated, and when the determination unit determines that the effect does not reach the specified requirement, the correction condition is satisfied, and the correction mode is turned on.
  • the effect of the old correction coefficient correction is evaluated by the root mean square error or image smoothness of all the sensing unit output data corrected by the old correction coefficient.
  • N and M are the number of rows and columns of the sensing unit in the fingerprint sensor
  • y is the data output by the sensing unit
  • x old is the data corrected by the sensing unit after the old correction coefficient
  • f is the corrected data of all the sensing units.
  • Image, h 1 [1, -1].
  • the fingerprint sensor may also determine whether the correction condition is satisfied by determining the current mode, and if it is detected that it is currently in the correction mode, it is determined that the correction condition is satisfied.
  • Step S310 When the calibration is started, the fingerprint signal is sensed, and the first data output by each sensing unit is acquired.
  • Step S320 Calculate a new correction coefficient of the corresponding sensing unit by using the first data according to the mathematical model.
  • Step S330 It is judged whether there is an old correction coefficient.
  • step S340 If there is an old correction coefficient, it proceeds to step S340; otherwise, it proceeds to step S350.
  • Step S340 The correction coefficient obtained by the current correction is replaced with the old correction coefficient as a new correction coefficient.
  • the fingerprint sensor When there is an old correction coefficient, the fingerprint sensor replaces the correction coefficient obtained by the current correction with the old correction coefficient as a new correction coefficient, and stores the new correction coefficient.
  • the new correction coefficient and the old correction coefficient may also be weighted and summed, and the calculation result is replaced with the old correction coefficient as a new correction coefficient and stored.
  • the correction coefficients obtained by this correction be k new and b new
  • the old correction coefficients are k old and b old
  • the new correction coefficients are k and b
  • the weights are 0.8 and 0.2
  • the weights can be set according to specific conditions. , there are:
  • the replacement method of the weighted summation of the old and new correction coefficients can speed up the convergence speed of the correction coefficient calculation and improve the robustness of the system.
  • Step S350 directly use the correction coefficient obtained by the current correction as the correction coefficient of the fingerprint sensor.
  • Step S360 Correct the second data output by the corresponding sensing unit according to the new correction coefficient.
  • the fingerprint sensor provided in the foregoing embodiment is only illustrated by the division of each functional module described above in the calibration. In actual applications, the function distribution may be completed by different functional modules as needed.
  • the fingerprint sensor and the method for correcting the fingerprint sensor are provided in the same concept, and the technical features in the method embodiment are applicable in the fingerprint sensor embodiment.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Image Input (AREA)
  • Collating Specific Patterns (AREA)
  • Measurement Of Length, Angles, Or The Like Using Electric Or Magnetic Means (AREA)

Abstract

本发明公开了一种指纹传感器及其校正方法，所述指纹传感器具有供手指接触的感应面，包括多个感应单元、处理单元和校正单元，其中：感应单元，用于开始校正时，感应指纹信号并输出第一数据；处理单元，用于根据数学模型，通过第一数据计算对应的感应单元新的校正系数；校正单元，用于根据新的校正系数，校正对应的感应单元输出的第二数据。本发明的指纹传感器能够在自身物理特性发生变化时自动修正校正系数，达到减小产品返修概率及延长产品使用寿命的效果，并且省掉了出厂前的校正环节，降低了生产难度及生产成本。

Description

指纹传感器及其校正方法 技术领域
本发明涉及指纹识别技术领域，尤其是涉及一种指纹传感器及其校正方法。
背景技术
指纹是手指表面皮肤凹凸不平形成的纹理，指纹的纹理特征具有唯一性、稳定性，因此常常被用来作为身份识别的依据。指纹传感器就是一种通过指纹来识别身份的传感器。
如图1所示，指纹传感器包括指纹传感芯片10和通过粘合剂30固定于指纹传感芯片10表面的保护盖20(Cover)，保护盖20用于保护指纹传感芯片10。指纹传感芯片10上具有多个感应单元11(Pixel)，如图2所示，多个感应单元11呈矩阵排布，感应单元11用于感应指纹信号并输出数据，该指纹信号即指纹纹理的深度信息。保护盖20表面作为指纹传感器的感应面，当手指按压感应面时，所有感应单元11输出的指纹纹理的深度信息组成的矩阵就是手指的纹理信息。
在实际环境中，同一指纹传感器中的不同感应单元11存在差异，加上粘合剂30的不均匀性及其杂质、保护盖20的平整度等，均会导致各感应单元11之间响应不均匀，因此不能通过指纹传感器输出的数据直接获取准确的指纹纹理特征，必须对输出的数据进行校正，以消除这种不均匀性。
为此，现有技术中的做法是在出厂前对指纹传感器进行一次校正操作，校正操作采用的是两点校正法。这种方法是基于感应单元11输出的数据和感应单元11的输入信号(即指纹纹理的深度信息)成线性关系，设感应单元11输出的数据为y，感应单元11输入的信号为x，则存在系数k、b满足直线方程式y＝kx+b，所以通过直线上的两个点P1(x1,y1)和P2(x2,y2)就能够得到如图3所示的直线方程。
两点校正法的具体流程为：首先获取指纹传感器空载时(即传感器的感应面上没有物体按压时)感应单元11输出的一帧数据，然后获取指纹传感器 上放有一块平整金属块50时感应单元11输出的一帧数据(如图4所示)。通过上面两帧数据对每一个感应单元11分别建立如下方程组：
Figure PCTCN2015094441-appb-000001
其中，P1(x1,y1)为空载时输出的数据，P2(x2,y2)为放金属块时输出的数据，解得：
Figure PCTCN2015094441-appb-000002
另外，如图3所示，x1为空载时的输入信号大小，即为0；x2为放金属块时的输入信号大小，可以定义为1。代入到方程(2)中即可求得：
Figure PCTCN2015094441-appb-000003
求得k、b值之后，就可以通过下式对输出的数据进行校正：
Figure PCTCN2015094441-appb-000004
其中N、M分别为指纹传感器中感应单元11的行数和列数，y为感应单元11输出的数据，x即为校正后的数据(即感应单元11的输入信号)。
然而，现有技术中对指纹传感器的校正方法，具有以下缺陷：
1)、必须在出厂前进行一次校正，校正环节提高了产品成本。
2)、对校正环境要求高：即需要使用非常平整的金属块去校正，校正时指纹传感器的感应面不能有灰尘等杂质。
3)、因为指纹传感器的物理特性会随着时间的变化而变化，而现有技术中仅仅在出厂前进行一次校正，当指纹传感器的物理特性发生变化时，之前校正获得的校正系数(即k、b值)将会失效，从而导致指纹传感器无法正常使用，缩短了产品的使用寿命。
发明内容
本发明的主要目的在于提供一种指纹传感器及其校正方法，旨在实现在使用过程中对指纹传感器进行校正，自动修正校正系数，以减小产品返修概率，延长产品使用寿命。
为达以上目的，本发明提出一种指纹传感器，具有供手指接触的感应面，包括多个感应单元、处理单元和校正单元，
感应单元，用于开始校正时，感应指纹信号并输出第一数据；
处理单元，用于根据数学模型，通过所述第一数据计算对应的感应单元新的校正系数；
校正单元，用于根据所述新的校正系数，校正对应的感应单元输出的第二数据。
本发明同时提出一种指纹传感器的校正方法，所述指纹传感器具有供手指接触的感应面，所述感应面包括多个感应单元，包括步骤：
S10：开始校正时，获取每个感应单元输出的第一数据；
S20：根据数学模型，通过所述第一数据计算对应的感应单元新的校正系数；
S30：根据所述新的校正系数，校正对应的感应单元输出的第二数据。
本发明所提供的一种指纹传感器，在使用过程中通过感应实际手指按压产生的指纹信号并获取感应单元输出的数据，以计算校正系数，从而能够在使用过程中随时进行校正操作，当指纹传感器的物理特性发生变化时，能够自动修正校正系数，从而达到减小产品返修概率及延长产品使用寿命的效果。并且，本发明实施例的指纹传感器简化或省掉了出厂前的校正环节，从而降低了生产难度及生产成本。
附图说明
图1是指纹传感器的结构示意图；
图2是感应单元的排布示意图；
图3是感应单元输出的数据与输入信号的函数关系示意图；
图4是现有技术中指纹传感器出厂前进行校正的示意图；
图5是本发明指纹传感器的模块示意图；
图6是本发明指纹传感器第一实施例的模块示意图；
图7是本发明指纹传感器第一实施例中手指按压指纹传感器的示意图；
图8是本发明指纹传感器第一实施例中手指多次按压指纹传感器的示意图；
图9是本发明指纹传感器第二实施例的模块示意图；
图10是本发明指纹传感器第三实施例的模块示意图；
图11是本发明指纹传感器的校正方法第一实施例的流程图；
图12是本发明指纹传感器的校正方法第二实施例的流程图；
图13是本发明指纹传感器的校正方法第三实施例的流程图。
本发明目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
参见图5提出本发明指纹传感器的模块示意图，该指纹传感器具有一供手指按压的感应面，感应面上通常覆盖了如图1所示的保护盖。所述感应面包括多个呈阵列排布的感应单元、一处理单元和一校正单元。
感应单元：用于开始校正时，感应指纹信号并输出第一数据。
处理单元：用于根据数学模型，通过第一数据计算对应的感应单元新的校正系数。
具体的，指纹传感器具有正常模式和校正模式两个模式。其中，正常模式用于进行正常的指纹感应并输出感应数据，校正模式用于对指纹感应器进行校正，获得校正系数。新的校正系数包括但不限于通过神经网络、数据统计、曲线拟合、数学模型等方式计算得到，如y＝kx+b线性模型。指纹传感器可以通过一模式切换单元来进行模式切换。
校正单元：用于根据新的校正系数，校正对应的感应单元输出的第二数据。
如图6所示，作为本发明的第一实施例，感应单元用于获取指纹传感器在空载时输出的一帧数据，还用于获取指纹传感器在手指按压感应面不同位置时输出的至少两帧数据，并将这些数据发送到处理单元。
当指纹传感器切换到校正模式后，感应单元感应指纹信号，获取一帧指纹传感器空载时(即感应面没有任何物体按压时)输出的第一数据。所有感应单元输出的第一数据即为一帧数据。然后提示用户用手指按压感应面的不同位置，当用户手指每按压一次感应面，则获取一帧感应单元输出的数据，即手指按压感应面多少次，则获取多少帧数据。
当指纹脊部(即指纹凸出的部分)按到感应面上时，即按到感应单元上，该感应单元输出的数据为最大值；而当指纹沟部(即指纹凹陷的部分)按到感应面上时，对应的感应单元输出的数据为一个较小的值。例如，当手指以如图7所示方式按压感应面时，感应单元11b、11h、11m、11n、11s将输出最大值，而其它感应单元则将输出较小的值。
多次按压时，用户手指每次按压感应面的位置、角度都是不同的，所以当用户多次用手指按压指纹传感器后，指纹传感器上的每个感应单元都可以被指纹脊部按到。手指按压感应面的次数，即非空载时获取的帧数，根据实际需要而定，通常感应面越大则需获取的帧数越多，感应面越小则需获取的帧数越少，总之以所有感应单元均被手指脊部按到为准。例如，如图8所示，经过手指5次按压后每个感应单元都曾被指纹脊部按压过，感应单元相应地获取5帧输出数据。
处理单元包括选取子单元和第二计算子单元，选取子单元用于从上述至少两帧数据中选取每个感应单元对应的最大值。第二计算子单元用于计算感应单元新的校正系数。
具体的，选取子单元从非空载时获取的至少两帧数据中选出每个感应单元对应的最大值，即相当于组成一帧所有感应单元均被指纹脊部按压时输出的数据，而这一帧数据与现有技术中进行校正时用平整金属块按压感应面时感应单元输出的数据近似，用这一帧数据和一帧空载时感应单元输出的数据就可以进行一次校正。
设空载时一个感应单元获取的数据为Dmin，非空载时该感应单元获取的数据的最大值为Dmax，手指按压感应面Num次(Num为常数，根据具体情况而定)，多个感应单元呈N行M列排布，则Dmaxi,j＝MAX(D1i,j,D2i,j,D3i,j,…,Dnumi,j)，其中Dnumi,j代表第i行第j列的感应单元输出的第Num帧的数据，i∈[1,N]，j∈[1,M]。
然后，第二计算子单元根据y＝kx+b线性模型计算校正系数k、b的值：
Figure PCTCN2015094441-appb-000005
若指纹传感器之前做过校正或者在出厂时做过初始校正，存储单元存储有校正系数，将本次校正获得的校正系数作为新的校正系数替换旧的校正系数，并将该新的校正系数存储起来；或者将旧的校正系数和本次校正获得的校正系数加权求和后替换旧的校正系数，并予以存储。设本次校正获得的校正系数为knew和bnew，旧的校正系数为kold和bold，新的校正系数为k和b，权值为0.8和0.2，权值可以根据具体情况设定，则有：
Figure PCTCN2015094441-appb-000006
Figure PCTCN2015094441-appb-000007
新旧校正系数加权求和后的替换方式，相对于直接替换方式，能够加快校正系数计算的收敛速度，并提高系统的鲁棒性。
若指纹传感器以前没有做过校正，本次校正为首次校正，则将本次获得的校正系数作为指纹传感器新的校正系数，并予以存储。
当指纹传感器切换至正常模式，感应指纹信息时，校正单元根据下式对感应单元输出的数据进行校正：
Figure PCTCN2015094441-appb-000008
其中N、M分别为指纹传感器中感应单元的行数和列数，y′为感应单元输出的第二数据，x即为经新的校正系数校正后的数据。
本发明第一实施例中的指纹传感器，通过获取至少两帧手指按压感应面不同位置时感应单元输出的数据，并从中选取出最大值，从而组成一帧所有感应单元均被指纹脊部按压时输出的数据，而这一帧数据与现有技术中进行校正时用平整金属块按压感应面时感应单元输出的数据近似，用这一帧数据和一帧空载时感应单元输出的数据就可以对指纹传感器进行一次校正，获得校正系数。本发明实施例的指纹传感器能够在使用过程中随时进行校正操作，当指纹传感器物理特性发生变化时能够自动修正校正系数，从而达到减小产品返修概率及延长产品使用寿命的效果。并且，本发明实施例的指纹传感器简化或省掉了出厂前的校正环节，从而降低了生产难度及生产成本。
参见图9，作为本发明的指纹传感器第二实施例，本实施例中，处理单元包括第一计算子单元，其用于根据数学模型，通过感应单元输出的第一数据和对应的旧的校正系数，计算新的校正系数。
Figure PCTCN2015094441-appb-000009
bnew＝(1-σ)bold +σ[y-Avg(Y)]
其中，knew 和bnew为感应单元新的校正系数，kold 和bold 为感应单元旧的校正系数，y为该感应单元输出的第一数据，Y为所有感应单元输出的第一数据组成的图像，F(Y)为对Y进行中值滤波后对应的感应单元的数据，Avg(Y)为对Y求平均值后对应的感应单元的数据，σ为学习因子，取值范围在[0,1]，
Figure PCTCN2015094441-appb-000010
xold 为感应单元经旧的校正系数校正后得到的数据。
当指纹传感器切换至正常模式，感应指纹信息时，校正单元根据下式对感应单元输出的数据进行校正：
Figure PCTCN2015094441-appb-000011
其中N、M分别为指纹传感器中感应单元的行数和列数，y′为感应单元输出的第二数据，x即为经新的校正系数校正后的数据。
本发明第二实施例中的指纹传感器，通过手指一次按压得到一帧第一数据，结合旧的校正系数计算得到新的校正系数。本发明实施例的指纹传感器能够在使用过程中随时进行校正操作，当指纹传感器物理特性发生变化时能够自动修正校正系数，从而达到减小产品返修概率及延长产品使用寿命的效果。
参见图10，作为本发明的第三实施例，与第一实施例和第二实施例的区别在于增加一判断单元。判断单元用于判断是否满足校正条件，当判定满足校正条件时则开启校正模式，开始校正操作。校正条件包括但不限于指纹传感器首次启用、到达预设的校正时间、接收到校正指令、经旧的校正系数校正的效果未达到指定要求中的一种。
判断单元检测到指纹传感器首次启动时，则判定为满足校正条件，开启校正模式。
可选地，可以设定校正时间，当判断单元检测到到达预设的校正时间时，则判定满足校正条件，开启校正模式。校正时间可以是具体的时刻，如设定某月某日某时进行一次校正；也可以是间隔时间，如设定每隔三个月进行一次校正。
可选地，可以评价经旧的校正系数校正的效果，当判断单元判定该效果未达到指定要求时，则满足校正条件，开启校正模式。经旧的校正系数校正的效果由所有感应单元输出数据经旧的校正系数校正后的均方根误差或者图像光滑度来评价。
Figure PCTCN2015094441-appb-000012
Figure PCTCN2015094441-appb-000013
其中N、M分别为指纹传感器中感应单元的行数和列数，y为感应单元输出的数据，xold为感应单元经旧的校正系数校正后的数据，f为所有感应单元校正后的数据图像，h1＝[1,-1]。当均方根误差或者图像光滑度大于预设阈值时，则判定为校正效果未达到指定要求。
此外，用户也可以手动启用校正模式。例如，用户通过菜单选项、功能或虚拟按键、触控指令、预设手势动作等发出校正指令，判断单元接收到该校正指令后，则判定满足校正条件，开启校正模式。
可选地，判断单元也可以通过判断当前的模式来判断是否满足校正条件，如检测到当前处于校正模式时，则判定满足校正条件。
可以理解，除了上述列举的例子外，还可以设定其它校正条件来启动校正模式，在此不再赘述。
参见图11，提出本发明指纹传感器的校正方法第一实施例，指纹传感器具有供手指接触的感应面，该感应面包括多个感应单元，该校正方法包括以下步骤：
步骤S100：切换至校正模式。
本实施例中，指纹传感器具有正常模式和校正模式两个模式。其中，正常模式用于进行正常的指纹感应并输出感应数据，校正模式用于对指纹传感器进行校正，获得校正系数。当指纹传感器切换到校正模式后，则开始进行校正操作。
步骤S110：获取指纹传感器在空载时感应单元输出的一帧数据。
指纹传感器具有一供手指按压的感应面，该感应面上通常覆盖有如图1所示的保护盖。指纹传感器包括多个呈阵列排布的感应单元，该感应单元用于感应指纹信号并输出数据。开始校正后，指纹传感器自动获取指纹传感器空载时(即感应面没有任何物体按压时)所有感应单元输出的一帧数据。
步骤S120：获取指纹传感器在手指按压感应面不同位置时感应单元输出的至少两帧数据。
指纹传感器可以提示用户用手指按压感应面的不同位置，当用户手指每按压一次感应面，则获取一帧所有感应单元输出的数据，即手指按压感应面 多少次，则获取多少帧第二数据。
在实际使用过程中，用户手指每次按压感应面的位置、角度都是不同的，所以当用户多次用手指按压指纹传感器后，指纹传感器上的每个感应单元都可以被指纹脊部按到。手指按压感应面的次数，即获取数据的帧数，根据实际需要而定，通常感应面越大则需获取的帧数越多，感应面越小则需获取的帧数越少，总之以所有感应单元均被手指脊部按到为准。例如，如图8所示，经过手指5次按压后每个感应单元都曾被指纹脊部按压过，相应的获取5帧感应单元输出的数据。
步骤S130：从上述至少两帧数据中选取每个感应单元对应的最大值。
指纹传感器从每个感应单元输出的至少两帧数据中选出最大值，就相当于组成一帧所有感应单元均被指纹脊部按压时输出的数据，而这一帧数据与现有技术中进行校正时用平整金属块按压感应面时感应单元输出的数据近似，用这一帧数据和一帧空载时的数据就可以进行一次校正。
设空载时的数据为Dmin，手指按压时的最大值为Dmax，手指按压感应面Num次(Num为常数，根据具体情况而定)，多个感应单元呈N行M列排布，则Dmaxi,j＝MAX(D1i,j,D2i,j,D3i,j,…,Dnumi,j)，其中Dnumi,j代表第i行第j列感应单元输出的第Num帧的数据，i∈[1,N]，j∈[1,M]。
步骤S140：计算感应单元新的校正系数： knew＝对应的最大值-空载时输出的数据，bnew＝空载时输出的数据。
将选取的最大值Dmax与Dmin，根据y＝kx+b线性模型计算获得校正系数k、b的值：
Figure PCTCN2015094441-appb-000014
Figure PCTCN2015094441-appb-000015
步骤S150：判断是否有旧的校正系数。
若指纹传感器以前做过校正或者在出厂时做过初始校正，存储有旧的校正系数，则进入步骤S160；若指纹传感器以前没有做过校正，本次校正为首次校正，不存在旧的校正系数，则进入步骤S170。
步骤S160：将本次校正获得的校正系数作为新的校正系数替换旧的校正系数。
当有旧的校正系数时，指纹传感器则将本次校正获得的校正系数作为新的校正系数替换旧的校正系数，并将该新的校正系数存储起来。
在某些实施例中，也可以对新的校正系数和旧的校正系数加权求和，将计算结果作为新的校正系数替换旧的校正系数，并予以存储。设本次校正获得的校正系数为knew和bnew，旧的校正系数为kold和bold，新的校正系数为k和b，权值为0.8和0.2，权值可以根据具体情况设定，则有：
Figure PCTCN2015094441-appb-000016
Figure PCTCN2015094441-appb-000017
新旧校正系数加权求和后的替换方式，相对于直接替换方式，能够加快校正系数计算的收敛速度，并提高系统的鲁棒性。
步骤S170：直接将本次校正获得的校正系数作为指纹传感器的校正系数。
当没有旧的校正系数时，指纹传感器则直接将本次获得的校正系数作为指纹传感器的校正系数，并予以存储。
至此校正完成。当指纹传感器切换至正常模式，感应指纹信息时，则根据下式对感应单元输出的数据进行校正：
Figure PCTCN2015094441-appb-000018
其中N、M分别为指纹传感器中感应单元的行数和列数，y′为感应单元输出的第二数据，x即为经新的校正系数校正后的数据。
在某些实施例中，也可以在步骤S130选取出最大值之后再执行步骤S110获取空载时的数据。
本发明实施例通过获取至少两帧手指按压感应面不同位置时感应单元输出的数据，并从中选取出最大值，从而组成一帧所有感应单元均被指纹脊部按压时输出的数据，而这一帧数据与现有技术中进行校正时用平整金属块按压感应面时感应单元输出的数据相同，用这一帧数据和一帧空载时感应单元输出的数据就可以对指纹传感器进行一次校正，获得校正系数。本发明实施例的指纹传感器能够在使用过程中随时进行校正操作，当指纹传感器特性发生变化时能够自动修正校正系数，从而达到减小产品返修概率及延长产品正常使用寿命的效果。并且，本发明实施例的指纹传感器简化或省掉了出厂前的校正环节，从而降低了生产难度及生产成本。
参见图12，提出本发明指纹传感器的校正方法第二实施例，校正方法包括以下步骤：
步骤S200：切换至校正模式。
步骤S210：开始校正时，感应指纹信号，获取每个感应单元输出的第一数据。
步骤S220：根据数学模型，通过第一数据和对应的感应单元旧的校正系数，计算对应的感应单元新的校正系数。
Figure PCTCN2015094441-appb-000019
bnew＝(1-σ)bold +σ[y-Avg(Y)]
其中，knew 和bnew为感应单元新的校正系数，kold 和bold 为感应单元旧的校正系数，y为该感应单元输出的第一数据，Y为所有感应单元输出的第一数据组成的图像，F(Y)为对Y进行中值滤波后对应的感应单元的数据，Avg(Y)为对Y求平均值后对应的感应单元的数据，σ为学习因子，取值范围在[0,1]，
Figure PCTCN2015094441-appb-000020
xold为感应单元经旧的校正系数校正后得到的数据。
步骤S230：判断是否有旧的校正系数。
若指纹传感器以前做过校正或者在出厂时做过初始校正，存储有旧的校正系数，则进入步骤S240；若指纹传感器以前没有做过校正，本次校正为首次校正，不存在旧的校正系数，则进入步骤S250。
步骤S240：将本次校正获得的校正系数作为新的校正系数替换旧的校正系数。
当有旧的校正系数时，指纹传感器则将本次校正获得的校正系数作为新的校正系数替换旧的校正系数，并将该新的校正系数存储起来。
在某些实施例中，也可以对新的校正系数和旧的校正系数加权求和，将计算结果作为新的校正系数替换旧的校正系数，并予以存储。设本次校正获得的校正系数为knew 和bnew ，旧的校正系数为kold 和bold ，新的校正系数为k和b，权值为0.8和0.2，权值可以根据具体情况设定，则有：
Figure PCTCN2015094441-appb-000021
Figure PCTCN2015094441-appb-000022
新旧校正系数加权求和后的替换方式，相对于直接替换方式，能够加快校正系数计算的收敛速度，并提高系统的鲁棒性。
步骤S250：直接将本次校正获得的校正系数作为指纹传感器的校正系数。
步骤S260：根据所述新的校正系数，校正对应的感应单元输出的第二数据。
当指纹传感器切换至正常模式，感应指纹信息时，根据下式对感应单元 输出的数据进行校正：
Figure PCTCN2015094441-appb-000023
其中N、M分别为指纹传感器中感应单元的行数和列数，y′为感应单元输出的第二数据，x即为经新的校正系数校正后的数据。
步骤S230、S240、S250分别与步骤S150、S160、S170一一对应并且相同，在此不再赘述。
本实施例通过手指一次按压得到一帧第一数据，结合旧的校正系数计算得到新的校正系数。本发明实施例的指纹传感器能够在使用过程中随时进行校正操作，当指纹传感器物理特性发生变化时能够自动修正校正系数，从而达到减小产品返修概率及延长产品使用寿命的效果。
参见图13，提出本发明指纹传感器的校正方法第三实施例，校正方法包括以下步骤：
步骤S300：判断是否满足校正条件。
本实施例通过设置校正条件来启用校正模式。当检测到指纹感应器首次启用、或者检测到到达预设的校正时间、或者接收到校正指令、或者经旧的校正系数校正的效果未达到指定要求，则判定满足校正条件，开启校正模式，进入步骤S310，开始校正操作。当判定不满足校正条件时，则继续进行侦测判断。
其中，当指纹传感器首次启用时，可以设置默认开启校正模式。可选地，可以设定校正时间，校正时间可以是具体的时刻，如设定某月某日某时进行校正；也可以是间隔时间，如设定每隔三天进行一次校正。还可以是，用户通过菜单选项、功能或虚拟按键、触控指令、预设手势动作等发出校正指令，手动启用校正模式。
可选地，可以评价经旧的校正系数校正的效果，当判断单元判定该效果未达到指定要求时，则满足校正条件，开启校正模式。经旧的校正系数校正的效果由所有感应单元输出数据经旧的校正系数校正后的均方根误差或者图像光滑度来评价。
Figure PCTCN2015094441-appb-000024
Figure PCTCN2015094441-appb-000025
其中N、M分别为指纹传感器中感应单元的行数和列数，y为感应单元输出的数据，xold为感应单元经旧的校正系数校正后的数据，f为所有感应单元校正后的数据图像，h1＝[1,-1]。当均方根误差或者图像光滑度大于预设阈值时，则判定为校正效果未达到指定要求。
可以理解，除了上述列举的例子外，还可以设定其它校正条件来启动校正功能，在此不再赘述。
可选地，指纹传感器也可以通过判断当前的模式来判断是否满足校正条件，如检测到当前处于校正模式时，则判定满足校正条件。
步骤S310：开始校正时，感应指纹信号，获取每个感应单元输出的第一数据。
步骤S320：根据数学模型，通过第一数据计算对应的感应单元新的校正系数。
步骤S330：判断是否有旧的校正系数。
如果有旧的校正系数，则进入步骤S340；否则，进入步骤S350。
步骤S340：将本次校正获得的校正系数作为新的校正系数替换旧的校正系数。
当有旧的校正系数时，指纹传感器则将本次校正获得的校正系数作为新的校正系数替换旧的校正系数，并将该新的校正系数存储起来。
在某些实施例中，也可以对新的校正系数和旧的校正系数加权求和，将计算结果作为新的校正系数替换旧的校正系数，并予以存储。设本次校正获得的校正系数为knew和bnew，旧的校正系数为kold和bold，新的校正系数为k和b，权值为0.8和0.2，权值可以根据具体情况设定，则有：
Figure PCTCN2015094441-appb-000026
Figure PCTCN2015094441-appb-000027
新旧校正系数加权求和后的替换方式，相对于直接替换方式，能够加快校正系数计算的收敛速度，并提高系统的鲁棒性。
步骤S350：直接将本次校正获得的校正系数作为指纹传感器的校正系数。
步骤S360：根据新的校正系数，校正对应的感应单元输出的第二数据。
需要说明的是：上述实施例提供的指纹传感器在进行校正时，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成。另外，上述实施例提供的指纹传感器与指纹传感器的校正方法实施例属于同一构思，且方法实施例中的技术特征在指纹传感器实施例中均对应适用。
本领域普通技术人员可以理解,实现上述实施例方法中的全部或部分步骤可以通过程序来控制相关的硬件完成，所述的程序可以存储于一计算机可读取存储介质中，所述的存储介质可以是ROM/RAM、磁盘、光盘等。
以上参照附图说明了本发明的优选实施例，并非因此局限本发明的权利范围。本领域技术人员不脱离本发明的范围和实质，可以有多种变型方案实现本发明，比如作为一个实施例的特征可用于另一实施例而得到又一实施例。凡在运用本发明的技术构思之内所作的任何修改、等同替换和改进，均应在本发明的权利范围之内。

Claims (14)

  1. 一种指纹传感器，具有供手指接触的感应面，所述感应面包括多个感应单元，其特征在于，所述指纹传感器还包括处理单元和校正单元，其中：
    感应单元，用于开始校正时，感应指纹信号并输出第一数据；
    处理单元，用于根据数学模型，通过所述第一数据计算对应的感应单元新的校正系数；
    校正单元，用于根据所述新的校正系数，校正对应的感应单元输出的第二数据。
  2. 根据权利要求1所述的指纹传感器，其特征在于，还包括存储单元，用于存储校正系数，当所述指纹传感器中存储有旧的校正系数时，以新的校正系数替换旧的校正系数，或者将新的校正系数和旧的校正系数加权求和后替换旧的校正系数。
  3. 根据权利要求2所述的指纹传感器，其特征在于，还包括判断单元，用于判断是否满足校正条件，所述校正条件包括所述指纹传感器首次启用、到达预设的校正时间、接收到校正指令、经旧的校正系数校正的效果未达到指定要求中的一种或者多种。
  4. 根据权利要求3所述的指纹传感器，其特征在于，所述经旧的校正系数校正的效果，由所有感应单元输出数据经旧的校正系数校正后的均方根误差或者图像光滑度来评价，当所述均方根误差或者图像光滑度大于预设阈值时，为未达到指定要求。
  5. 根据权利要求1-4任一项所述的指纹传感器，其特征在于，所述处理单元包括第一计算子单元，用于根据数学模型，通过所述第一数据和对应的感应单元旧的校正系数，计算对应的感应单元新的校正系数，
    Figure PCTCN2015094441-appb-100001
    bnew＝(1-σ)bold+σ[y-Avg(Y)]
    其中，knew和bnew为感应单元新的校正系数，kold和bold为感应单元旧的校正系数，y为该感应单元输出的第一数据，Y为所有感应单元输出的第一数据组成的图像，F(Y)为对Y进行中值滤波后对应的感应单元的数据，Avg(Y)为对Y求平均值后对应的感应单元的数据，σ为学习因子，取值范围在[0,1]，
    Figure PCTCN2015094441-appb-100002
    xold为感应单元经旧的校正系数校正后得到的数据。
  6. 根据权利要求1-4任一项所述的指纹传感器，其特征在于，
    所述感应单元还用于：获取所述指纹传感器在空载时输出的一帧数据，获取所述指纹传感器在手指按压所述感应面不同位置时输出的至少两帧数据；
    所述处理单元包括：
    选取子单元，用于从所述至少两帧数据中选取每个感应单元对应的最大值；
    第二计算子单元，用于计算感应单元新的校正系数：knew＝对应的最大值-空载时输出的数据，bnew＝空载时输出的数据。
  7. 根据权利要求5或6所述的指纹传感器，其特征在于，所述校正单元具体用于：
    计算
    Figure PCTCN2015094441-appb-100003
    其中，knew和bnew为感应单元新的校正系数，y′为感应单元输出的第二数据，xnew为感应单元经新的校正系数校正后得到的数据。
  8. 一种指纹传感器的校正方法，所述指纹传感器具有供手指接触的感应面，所述感应面包括多个感应单元，其特征在于，包括步骤：
    S10：开始校正时，感应指纹信号，获取每个感应单元输出的第一数据；
    S20：根据数学模型，通过所述第一数据计算对应的感应单元新的校正系数；
    S30：根据所述新的校正系数，校正对应的感应单元输出的第二数据。
  9. 如权利要求8所述的校正方法，其特征在于，在所述步骤S20之后、步骤S30之前，还包括：
    当所述指纹传感器中存储有旧的校正系数时，以新的校正系数替换旧的校正系数，或者将新的校正系数和旧的校正系数加权求和后替换旧的校正系数。
  10. 根据权利要求9所述的校正方法，其特征在于，在所述步骤S10之前，还包括：
    判断是否满足校正条件，当满足校正条件时执行步骤S10，所述校正条件包括所述指纹传感器首次启用、到达预设的校正时间、接收到校正指令、经旧的校正系数校正的效果未达到指定要求。
  11. 根据权利要求10所述的校正方法，其特征在于，所述经旧的校正系数校正的效果，由所有感应单元输出数据经旧的校正系数校正后的均方根误差或者图像光滑度来评价，当所述均方根误差或者图像光滑度大于预设阈值时，为未达到指定要求。
  12. 根据权利要求8-11任一项所述的校正方法，其特征在于，所述步骤S20具体为：根据数学模型，通过所述第一数据和对应的感应单元旧的校正系数，计算对应的感应单元新的校正系数，
    Figure PCTCN2015094441-appb-100004
    bnew＝(1-σ)bold+σ[y-Avg(Y)]
    其中，knew和bnew为感应单元新的校正系数，kold和bold为感应单元旧的校正系数，y为该感应单元输出的第一数据，Y为所有感应单元输出的第一数据组成的图像，F(Y)为对Y进行中值滤波后对应的感应单元的数据，Avg(Y)为对Y求平均值后对应的感应单元的数据，σ为学习因子，取值范围在[0,1]，
    Figure PCTCN2015094441-appb-100005
    xold为感应单元经旧的校正系数校正后得到的数据。
  13. 根据权利要求8-11任一项所述的校正方法，其特征在于，
    所述步骤S10具体为：
    获取所述指纹传感器在空载时感应单元输出的一帧数据；
    获取所述指纹传感器在手指按压所述感应面不同位置时感应单元输出的至少两帧数据；
    所述步骤S20具体为：
    从所述至少两帧数据中选取每个感应单元对应的最大值；
    计算感应单元新的校正系数：knew＝对应的最大值-空载时输出的数据，bnew＝空载时输出的数据。
  14. 根据权利要求12或13所述的校正方法，其特征在于，所述步骤S30具体为：
    根据公式
    Figure PCTCN2015094441-appb-100006
    校正所述第二数据，其中，knew和bnew为感应单元新的校正系数，y′为感应单元输出的第二数据，xnew为感应单元经新的校正系数校正后得到的数据。
PCT/CN2015/094441 2015-01-13 2015-11-12 指纹传感器及其校正方法 WO2016112737A1 (zh)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP15877648.4A EP3182329B1 (en) 2015-01-13 2015-11-12 Fingerprint sensor and correction method thereof
KR1020177007213A KR101875349B1 (ko) 2015-01-13 2015-11-12 지문 센서 및 그 보정 방법
US15/459,575 US10643048B2 (en) 2015-01-13 2017-03-15 Fingerprint sensor and correction method thereof

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510016150.6A CN104700070B (zh) 2015-01-13 2015-01-13 指纹传感器及其校正方法
CN201510016150.6 2015-01-13

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US15/459,575 Continuation US10643048B2 (en) 2015-01-13 2017-03-15 Fingerprint sensor and correction method thereof

Publications (1)

Publication Number Publication Date
WO2016112737A1 true WO2016112737A1 (zh) 2016-07-21

Family

ID=53347169

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/094441 WO2016112737A1 (zh) 2015-01-13 2015-11-12 指纹传感器及其校正方法

Country Status (5)

Country Link
US (1) US10643048B2 (zh)
EP (1) EP3182329B1 (zh)
KR (1) KR101875349B1 (zh)
CN (2) CN107358188B (zh)
WO (1) WO2016112737A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114237427A (zh) * 2022-02-22 2022-03-25 深圳市赛元微电子有限公司 一种高灵敏度触控压力检测方法及系统

Families Citing this family (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10713466B2 (en) * 2014-03-07 2020-07-14 Egis Technology Inc. Fingerprint recognition method and electronic device using the same
CN107358188B (zh) * 2015-01-13 2021-12-07 深圳市汇顶科技股份有限公司 指纹传感器及其校正方法
CN104992158B (zh) * 2015-07-13 2020-11-13 格科微电子（上海）有限公司 提高光学指纹识别性能的方法
CN105260721A (zh) * 2015-10-19 2016-01-20 广东欧珀移动通信有限公司 一种指纹传感器的校准方法及装置
CN105243370A (zh) * 2015-10-19 2016-01-13 广东欧珀移动通信有限公司 一种指纹识别方法、指纹识别装置和移动终端
CN107609540B (zh) * 2015-10-19 2024-01-23 Oppo广东移动通信有限公司 一种指纹传感器的校准参数的获取方法、装置及移动终端
CN105243382A (zh) * 2015-10-19 2016-01-13 广东欧珀移动通信有限公司 一种指纹传感器校准方法和装置
CN106767628B (zh) * 2015-12-18 2019-04-19 深圳市汇顶科技股份有限公司 一种指纹传感器保护层的厚度检测方法及系统
CN106815564A (zh) * 2016-12-28 2017-06-09 深圳天珑无线科技有限公司 一种指纹识别系统的校准方法、系统及一种电子设备
KR102046701B1 (ko) * 2017-07-17 2019-11-19 선전 구딕스 테크놀로지 컴퍼니, 리미티드 광학 유도 보정 파라미터 확정 방법, 생체 특징 인증 장치 및 그의 전자 단말
CN107886083A (zh) * 2017-11-27 2018-04-06 北京小米移动软件有限公司 指纹模组校准的方法、装置和可读存储介质
KR20190088679A (ko) * 2018-01-19 2019-07-29 삼성전자주식회사 지문 입력의 압력 레벨에 기반하여 지문 처리 방식을 결정하는 전자 장치 및 방법
CN108446663A (zh) * 2018-04-02 2018-08-24 北京小米移动软件有限公司 指纹模组设置方法及装置
TWI693553B (zh) * 2018-12-18 2020-05-11 廣州印芯半導體技術有限公司 指紋感測裝置以及指紋感測方法
KR20200076984A (ko) 2018-12-20 2020-06-30 주식회사 바이오로그디바이스 지문인식센서 자동 보정 시스템
KR20210037783A (ko) * 2019-09-27 2021-04-07 삼성디스플레이 주식회사 표시 장치 및 이를 이용한 지문 센싱 데이터의 보상 방법
KR20220005677A (ko) 2020-07-06 2022-01-14 삼성디스플레이 주식회사 광학 센서를 포함하는 표시 장치 및 광학 센서의 위치 측정 방법
CN113625895A (zh) * 2021-07-02 2021-11-09 北京极豪科技有限公司 电子设备、图案校正方法及遮挡装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005156347A (ja) * 2003-11-26 2005-06-16 Alps Electric Co Ltd 容量検出回路及びその検出方法並びにそれを用いた指紋センサ
CN101089574A (zh) * 2006-06-14 2007-12-19 昆山双桥传感器测控技术有限公司 压力传感器误差修正方法
CN101329727A (zh) * 2008-06-27 2008-12-24 哈尔滨工业大学 点线结合的指纹识别方法
CN103870817A (zh) * 2014-03-27 2014-06-18 成都费恩格尔微电子技术有限公司 一种射频式微电容指纹采集芯片及采集方法
CN104700070A (zh) * 2015-01-13 2015-06-10 深圳市汇顶科技股份有限公司 指纹传感器及其校正方法

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3859673B2 (ja) * 2002-09-17 2006-12-20 富士通株式会社 生体情報取得装置および生体情報による認証装置
US8032348B2 (en) * 2003-09-30 2011-10-04 Tokyo Electron Limited System and method for using first-principles simulation to facilitate a semiconductor manufacturing process
US7504833B1 (en) * 2005-04-01 2009-03-17 Cypress Semiconductor Corporation Automatically balanced sensing device and method for multiple capacitive sensors
US7529391B2 (en) * 2005-12-29 2009-05-05 Microsoft Corporation Signature verification
FR2919962B1 (fr) * 2007-08-10 2009-09-18 Commissariat Energie Atomique Procede de correction de sensibilite et capteur d'image matriciel pour la mise en oeuvre de ce procede
CN101231692A (zh) 2007-12-24 2008-07-30 浙江金指码科技有限公司 通过指纹特征值调整的学习型智能指纹识别比对方法
CN101231691A (zh) 2007-12-24 2008-07-30 浙江金指码科技有限公司 通过指纹数据数量调整的学习型智能指纹识别比对方法
CN101819630B (zh) * 2010-04-09 2012-11-28 浙江理工大学 基于压感指纹采集和dsp算法的指纹识别方法
JP5713023B2 (ja) * 2010-12-03 2015-05-07 富士通株式会社 生体認証装置及び生体認証方法
CN102254167B (zh) 2011-08-16 2013-06-19 杭州晟元芯片技术有限公司 一种降低指纹比对拒真率的方法
CN102708360A (zh) 2012-05-09 2012-10-03 深圳市亚略特生物识别科技有限公司 一种指纹模板生成及自动更新的方法
JP5971089B2 (ja) * 2012-11-14 2016-08-17 富士通株式会社 生体情報補正装置、生体情報補正方法及び生体情報補正用コンピュータプログラム
CN103810479B (zh) * 2014-02-28 2019-04-05 成都费恩格尔微电子技术有限公司 指纹采集系统及指纹信息采集方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005156347A (ja) * 2003-11-26 2005-06-16 Alps Electric Co Ltd 容量検出回路及びその検出方法並びにそれを用いた指紋センサ
CN101089574A (zh) * 2006-06-14 2007-12-19 昆山双桥传感器测控技术有限公司 压力传感器误差修正方法
CN101329727A (zh) * 2008-06-27 2008-12-24 哈尔滨工业大学 点线结合的指纹识别方法
CN103870817A (zh) * 2014-03-27 2014-06-18 成都费恩格尔微电子技术有限公司 一种射频式微电容指纹采集芯片及采集方法
CN104700070A (zh) * 2015-01-13 2015-06-10 深圳市汇顶科技股份有限公司 指纹传感器及其校正方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3182329A4 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114237427A (zh) * 2022-02-22 2022-03-25 深圳市赛元微电子有限公司 一种高灵敏度触控压力检测方法及系统
CN114237427B (zh) * 2022-02-22 2022-05-13 深圳市赛元微电子有限公司 一种高灵敏度触控压力检测方法及系统

Also Published As

Publication number Publication date
CN107358188A (zh) 2017-11-17
KR101875349B1 (ko) 2018-07-05
US20170185819A1 (en) 2017-06-29
CN107358188B (zh) 2021-12-07
EP3182329B1 (en) 2020-03-04
CN104700070B (zh) 2017-09-12
EP3182329A1 (en) 2017-06-21
CN104700070A (zh) 2015-06-10
EP3182329A4 (en) 2017-11-22
US10643048B2 (en) 2020-05-05
KR20170044683A (ko) 2017-04-25

Similar Documents

Publication Publication Date Title
WO2016112737A1 (zh) 指纹传感器及其校正方法
CN108571804B (zh) 空调器及其参数调整方法、装置和可读存储介质
CN105335707B (zh) 一种待识别指纹图像的获取方法、装置及移动终端
CN101840265B (zh) 视觉感知装置及其控制方法
CN107124543B (zh) 一种拍摄方法及移动终端
US9424769B2 (en) Display and brightness adjusting method thereof
EP3637763B1 (en) Colour detection method and terminal
CN113936324A (zh) 注视检测方法、电子设备的控制方法及相关设备
JP2007087346A5 (zh)
CN106250883B (zh) 压力指纹识别方法、装置及终端设备
JP2007087345A5 (zh)
US8090158B2 (en) Image evaluation apparatus, method, and program
WO2018141119A1 (zh) 一种指纹采集的方法、装置及终端
WO2016072965A1 (en) Method and system for calibrating an eye tracking system
US11036336B2 (en) Display input device and image forming apparatus
CN113012407A (zh) 一种基于机器视觉的眼屏距离提示预防近视系统
CN112763072A (zh) 一种热成像校正方法、装置及终端设备
CN115690400A (zh) 一种红外图像显示方法
US20190122023A1 (en) Improvement of image quality in a fingerprint detecting apparatus and method
CN108848265B (zh) 局部区域的屏幕亮度调节方法、装置、终端及存储介质
CN112991469A (zh) 一种人脸亮度补偿方法、装置及计算机可读存储介质
US11400363B2 (en) Information processing apparatus, information processing system, controller device, information processing method, and program
CN109559707A (zh) 显示面板的伽马值处理方法、装置及显示设备
CN114862947A (zh) 一种距离确定方法以及距离确定装置
EP4016479B1 (en) Display device and driving method thereof

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15877648

Country of ref document: EP

Kind code of ref document: A1

REEP Request for entry into the european phase

Ref document number: 2015877648

Country of ref document: EP

WWE Wipo information: entry into national phase

Ref document number: 2015877648

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 20177007213

Country of ref document: KR

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE