WO2017152549A1 - 一种指纹识别的方法及终端 - Google Patents

一种指纹识别的方法及终端 Download PDF

Info

Publication number
WO2017152549A1
WO2017152549A1 PCT/CN2016/087778 CN2016087778W WO2017152549A1 WO 2017152549 A1 WO2017152549 A1 WO 2017152549A1 CN 2016087778 W CN2016087778 W CN 2016087778W WO 2017152549 A1 WO2017152549 A1 WO 2017152549A1
Authority
WO
WIPO (PCT)
Prior art keywords
fingerprint
data
capacitance value
fingerprint data
capacitance
Prior art date
Application number
PCT/CN2016/087778
Other languages
English (en)
French (fr)
Inventor
周意保
Original Assignee
广东欧珀移动通信有限公司
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 广东欧珀移动通信有限公司 filed Critical 广东欧珀移动通信有限公司
Priority to ES16893194T priority Critical patent/ES2763848T3/es
Priority to EP16893194.7A priority patent/EP3296922B1/en
Publication of WO2017152549A1 publication Critical patent/WO2017152549A1/zh
Priority to US15/846,236 priority patent/US10572713B2/en
Priority to US15/988,928 priority patent/US10169640B2/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
    • G06V40/1347—Preprocessing; Feature extraction
    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks
    • 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
    • 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
    • G06V40/1376—Matching features related to ridge properties or fingerprint texture

Definitions

  • the present invention relates to the field of communications technologies, and in particular, to a fingerprint identification method and terminal.
  • fingerprint recognition technology has become one of the standard models of mainstream terminal manufacturers. Fingerprint recognition technology can be used not only for wake-up or unlocking of terminals, but also as an important part of mobile payment.
  • the fingerprint recognition process in the fingerprint recognition technology includes: fingerprint feature data extraction, fingerprint feature data storage, and fingerprint matching.
  • the embodiment of the invention provides a fingerprint identification method and a terminal, which can reduce the difficulty of fingerprint recognition, improve the efficiency of fingerprint recognition, and enhance the user experience of the terminal.
  • An embodiment of the present invention provides a fingerprint identification method, which may include:
  • fingerprint simulation data and the fingerprint verification data match successfully, it is determined that the source fingerprint data is successfully identified.
  • FIG. 1 is a schematic flowchart of a method for fingerprint identification according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
  • FIG. 3 is another schematic structural diagram of a terminal according to an embodiment of the present invention.
  • the terminal described in the embodiment of the present invention may include a smart phone, a tablet computer, a personal digital assistant (PDA), a mobile internet device (MID), and a smart wear device (such as a smart watch).
  • PDA personal digital assistant
  • MID mobile internet device
  • smart wear device such as a smart watch
  • Various types of devices, such as smart bracelets, are not limited in the embodiment of the present invention.
  • the method and terminal for fingerprint identification provided by the embodiments of the present invention will be specifically described below by taking a mobile phone as an example.
  • fingerprint identification data needs to be extracted first, and then the fingerprint feature data is initially processed to obtain clearer fingerprint feature data, and then the feature data of the processed fingerprint feature data and the pre-stored fingerprint template are matched. If the fingerprint feature data obtained by the processing is successfully matched with the pre-stored fingerprint template, the fingerprint recognition can be completed, and then the terminal wakes up or unlocks.
  • the fingerprint feature data is extracted to obtain a fingerprint image, and the fingerprint image is processed to obtain a clear image, and the fingerprint feature in the image is not processed, and the processing method is simple. Fingerprint matching is performed by image matching between the fingerprint image and the fingerprint template image. Since the fingerprint data processing is only image processing, the fingerprint feature is not processed, and the integrity of the acquired fingerprint feature cannot be ensured, and the fingerprint matching recognition error rate is large. , the matching efficiency is low.
  • FIG. 1 is a fingerprint identification method according to an embodiment of the present invention.
  • fingerprint recognition may include overall feature recognition of the fingerprint and local feature recognition of the fingerprint, and the like.
  • the overall characteristics of the fingerprints refer to those directly observable by the human eye, including: basic texture patterns, such as ring lines, arcuate lines or spiral lines.
  • the local feature of the fingerprint refers to the node features such as the breakpoint, the bifurcation point or the turning point of the fingerprint texture.
  • the local feature of the fingerprint provides the confirmation node characteristic of the fingerprint uniqueness.
  • the mobile phone can acquire fingerprint data of the user's finger through its built-in fingerprint module.
  • the fingerprint module is composed of a fingerprint chip, and the fingerprint chip is internally composed of pixels including m*n queues, wherein m and n are natural numbers.
  • the capacitance value corresponding to each pixel in the image acquisition queue on the surface of the fingerprint module may be acquired.
  • the image acquisition queue is a pixel point queue composed of m*n pixels.
  • the mobile phone can obtain the capacitance values of the respective capacitors formed between the respective pixel points and the respective fingerprint peak points in the fingerprint texture, wherein the capacitance formed between one pixel point and one fingerprint peak point has a capacitance value. Since the fingerprint peak point of the finger fingerprint has a plurality of fingerprint peak points corresponding to one capacitance value, the mobile phone can set each of the acquired capacitance values corresponding to the respective fingerprint peak points as the first capacitance value.
  • the mobile phone can also obtain the capacitance value of each capacitor formed between each pixel point and the fingerprint valley point in the fingerprint pattern, wherein the capacitance formed between one pixel point and one fingerprint valley point has a capacitance value. Since the fingerprint fingerprint has a plurality of fingerprint valley points, each fingerprint valley point corresponds to a capacitance value, and the mobile phone can set each of the acquired capacitance values corresponding to the respective fingerprint valley points to the second capacitance value.
  • the first capacitance value and the second capacitance value may be set as source fingerprint data for forming an analog fingerprint, according to the source fingerprint data. Perform fingerprint matching and identification.
  • the representation form of the source fingerprint data is a fingerprint texture.
  • the capacitance value and the second capacitance value corresponding to each fingerprint peak point in the first capacitance value are The size of the capacitance value corresponding to each fingerprint valley point will be greatly different, and the fingerprint module of the mobile phone
  • the simulated fingerprint is formed according to the first capacitance value and the second capacitance value, an uneven three-dimensional surface can be formed, and the fingerprint texture image can be simulated through the three-dimensional surface.
  • the mobile phone may set a threshold range of the capacitance value according to the magnitude of each capacitance value in the obtained source fingerprint data, wherein the threshold range of the capacitance value may include more than 98% of the pixel points.
  • the mobile phone may extract, from the source fingerprint data obtained, the fingerprint data to be processed in which the fingerprint data value is within the preset threshold range, wherein the fingerprint data value may specifically be a fingerprint and each pixel of the fingerprint module.
  • the value of the capacitor By extracting the fingerprint data of the fingerprint data whose fingerprint data value is within a preset threshold range from the source fingerprint data, the abnormal data is eliminated, and the workload of the subsequent processing of the fingerprint data to be processed is reduced, thereby improving the efficiency of fingerprint recognition.
  • S102 Perform feature enlargement processing on each of the to-be-processed fingerprint data, and perform repair on the fingerprint data obtained by the enlargement process to obtain the repaired target fingerprint data.
  • the fingerprint data of the to-be-processed fingerprint data may be subjected to feature enlargement processing, and The feature of the fingerprint data is enlarged to enhance the recognizability of the fingerprint.
  • the specific representation of the feature amplification of the fingerprint data may be an amplification of the fingerprint texture.
  • the to-be-processed fingerprint data may include a first capacitance value and a second capacitance value within the preset threshold range after being filtered, wherein the first capacitance value is a capacitance value corresponding to a fingerprint peak point, The second capacitance value is a capacitance value corresponding to the fingerprint valley point.
  • the mobile phone may determine a median value of the first capacitance value and the second capacitance value according to the size of each of the capacitance values included in the first capacitance value and the second capacitance value of the to-be-processed fingerprint data, that is, each of the to-be-processed fingerprint data.
  • the 50-bit value of the capacitance value may be determined.
  • the median value can be set as the amplification reference value.
  • the amplifying reference value is used to process the to-be-processed fingerprint data into a series of data floating up and down around a median value, for enhancing the difference of features.
  • the fingerprint data to be processed may be subtracted from the zoom-in reference value to obtain fingerprint data to be amplified, and the to-be-amplified processing may be performed.
  • the fingerprint data is magnified to highlight various fingerprint features.
  • the mobile phone may multiply the fingerprint data to be amplified by a specified coefficient, and then multiply the specified coefficient by The fingerprint data is added to the above-mentioned enlarged reference value to obtain fingerprint data after the enlargement processing.
  • the specified coefficient is a multiple of the fingerprint feature, and the size of the specified coefficient may be determined according to an actual application scenario, and is not limited herein.
  • the mobile phone enlarges the to-be-processed fingerprint data and then adds back the above-mentioned enlarged reference value to obtain the fingerprint data after the enlargement processing, so that the feature difference of the fingerprint data is much larger than the feature difference of the fingerprint data before the enlargement.
  • the fingerprint data to be processed is first subtracted from the enlarged reference value, and then amplified, which can make the fingerprint peak point and the fingerprint valley point in the fingerprint texture clearer, and the fingerprint peak point and fingerprint in the fingerprint texture.
  • the excessive texture of the valley point (the pixel position corresponding to the median value of the capacitance) becomes a gray area, which increases the differentiation of the fingerprint texture. If the fingerprint data to be processed is directly enlarged, the fingerprint peak point, the fingerprint valley point, and the intermediate excessive texture will be enlarged, and the difference of the features cannot be highlighted, and thus the fingerprint data with clearer texture cannot be obtained.
  • the fingerprint data acquired by the fingerprint module is displayed in the fingerprint texture. A blank area will appear, and if the fingerprint data is not patched, the fingerprint will fail to be identified.
  • the fingerprint data in each pixel point area may be detected to determine whether the fingerprint data of the normal texture is normal.
  • the mobile phone may divide all the pixels of the fingerprint module into a plurality of pixel point regions, and each pixel dot region is a designated region, wherein each pixel region includes a pixel number of x*y, wherein , x and y are all natural numbers, which can be determined according to the actual application scenario, and no limitation is imposed here.
  • the capacitance value of each pixel included in each pixel area can be obtained, wherein the capacitance value of each pixel includes the capacitance value and the fingerprint corresponding to the peak of the fingerprint. The capacitance value corresponding to the valley point.
  • the mobile phone After the mobile phone obtains the capacitance value corresponding to the fingerprint peak point of each pixel area and the capacitance value corresponding to the fingerprint valley point, it may be determined that the capacitance value corresponding to the fingerprint peak point is between the capacitance value corresponding to the fingerprint valley point adjacent thereto The difference. If the fingerprint peak point of the certain pixel area and the maximum theoretical difference of the capacitance value corresponding to the fingerprint valley point are within the range of the maximum theoretical difference, the pixel area may be determined to be a normal fingerprint area, and the fingerprint data need not be repaired.
  • the maximum theoretical difference value may be determined in advance by a plurality of tests, that is, a maximum difference range between a capacitance value corresponding to a fingerprint peak point in a normal fingerprint texture determined by a plurality of tests and a capacitance value corresponding to a fingerprint valley point. There is no limit here.
  • the pixel area may be determined as an abnormal area, and the fingerprint data needs to be repaired.
  • a preset difference threshold ie, the maximum theoretical difference range
  • the fingerprint valley point is added with a corresponding capacitance value as a median value of the capacitance value corresponding to each pixel point in the pixel point region, so as to complete the texture of the fingerprint region where the abnormality occurs, and obtain the target fingerprint data of the complete fingerprint texture.
  • S103 Generate fingerprint simulation data according to the target fingerprint data, and match the fingerprint simulation data with pre-stored fingerprint verification data.
  • a three-dimensional surface may be generated according to the capacitance value of each pixel point included in the target fingerprint data. Since the capacitance value of each pixel is different, the three-dimensional surface generated according to the target fingerprint data will be an uneven three-dimensional surface, and the fingerprint texture image is simulated by the three-dimensional surface.
  • the simulated fingerprint texture image may be matched with the fingerprint verification data pre-stored in the mobile phone to determine whether the simulated fingerprint texture image matches the fingerprint image presented by the fingerprint verification data.
  • the pre-stored fingerprint verification data is fingerprint data such as a fingerprint image that is registered by the user in advance and stored in a designated storage space of the mobile phone.
  • the mobile phone may determine that the fingerprint data is successfully identified, and thereby perform functions such as unlocking or waking up the mobile phone. .
  • the mobile phone may first extract, from the source fingerprint data of the acquired fingerprint, each of the to-be-processed fingerprint data whose fingerprint data value is within a preset threshold range, and perform amplification processing and repairing the to-be-processed fingerprint data.
  • the target fingerprint data is processed, and then the fingerprint simulation image is generated according to the target fingerprint data, and the fingerprint verification data is matched with the fingerprint verification data such as the registered fingerprint image to determine whether the fingerprint recognition is successful. If the fingerprint recognition is successful, the corresponding mobile phone function can be performed.
  • the mobile phone obtains more complete target fingerprint data by amplifying and repairing the fingerprint data, which can be reduced.
  • the mobile phone reduces simulated fingerprint data by amplifying processing and repairing the target fingerprint data, which can improve the accuracy of fingerprint recognition, improve the efficiency of fingerprint recognition, and thereby improve the applicability of fingerprint recognition and enhance the user experience of the terminal.
  • FIG. 2 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
  • the terminal described in the embodiment of the present invention includes:
  • the extraction module 10 is configured to obtain source fingerprint data of the fingerprint identification, and extract, from the source fingerprint data, each to-be-processed fingerprint data whose fingerprint data value is within a preset threshold range.
  • the processing module 20 is configured to perform feature enlargement processing on each of the to-be-processed fingerprint data acquired by the extraction module, and repair the fingerprint data obtained by the amplification process to obtain the repaired target fingerprint data.
  • the matching module 30 is configured to generate fingerprint simulation data according to the target fingerprint data processed by the processing module, and match the fingerprint simulation data with pre-stored fingerprint verification data.
  • the determining module 40 is configured to determine that the source fingerprint data is successfully identified when the fingerprint simulation data is successfully matched with the fingerprint verification data.
  • the foregoing extraction module 10 is specifically configured to:
  • the first capacitance value and the second capacitance value are set as source fingerprint data for forming an analog fingerprint.
  • the to-be-processed fingerprint data is the first capacitance value and the second capacitance value within the preset threshold range
  • the processing module 20 is specifically configured to:
  • the foregoing processing module 20 is specifically configured to:
  • the capacitance value corresponding to any fingerprint peak point and the capacitance value corresponding to the fingerprint valley point adjacent to the fingerprint data is greater than a preset difference threshold, the capacitance value corresponding to the fingerprint peak point and The capacitance value corresponding to the fingerprint valley point is replaced by the median value of the capacitance value corresponding to each pixel point in the specified area.
  • the foregoing matching module 30 is specifically configured to:
  • the fingerprint identification described in the embodiments of the present invention may include overall feature recognition of the fingerprint, local feature recognition of the fingerprint, and the like.
  • the overall characteristics of the fingerprints refer to those directly observable by the human eye, including: basic texture patterns, such as ring lines, arcuate lines or spiral lines.
  • the local feature of the fingerprint refers to the node features such as the breakpoint, the bifurcation point or the turning point of the fingerprint texture.
  • the local feature of the fingerprint provides the confirmation node characteristic of the fingerprint uniqueness.
  • the extraction module 10 of the mobile phone can acquire the fingerprint data of the user's finger through the fingerprint module built in the mobile phone.
  • the fingerprint module is composed of a fingerprint chip, and the fingerprint chip internally comprises m*n pixels, wherein the m*n pixels are arranged in a queue, wherein m and n are natural numbers.
  • the extraction module 10 detects that the user's finger is pressed on the surface of the fingerprint module, the capacitance value corresponding to each pixel in the image acquisition queue on the surface of the fingerprint module may be acquired.
  • the image acquisition queue is a pixel point queue composed of m*n pixels.
  • the extraction module 10 can obtain capacitance values of respective capacitors formed between each pixel point and each fingerprint peak point in the fingerprint texture, wherein a capacitance formed between a pixel point and a fingerprint peak point has A capacitor value.
  • the fingerprint module may set the respective capacitance values corresponding to the acquired fingerprint peak points to the first capacitance value, because the fingerprint peaks of the finger fingerprints have a plurality of fingerprint peaks corresponding to one capacitance value.
  • the extraction module 10 can also obtain the capacitance values of the respective capacitances formed between the respective pixel points and the fingerprint valley points in the fingerprint texture, wherein the capacitance formed between one pixel point and one fingerprint valley point has a capacitance value.
  • the fingerprint module may set the respective capacitance values corresponding to the acquired fingerprint valley points to the second capacitance value.
  • the first capacitance value and the second capacitance value may be set as source fingerprint data for forming an analog fingerprint, according to the source.
  • Fingerprint data is fingerprint matched and identified. Since the distance between the fingerprint peak point and the pixel point of the fingerprint module is relatively close, the distance between the fingerprint valley point and the pixel point of the fingerprint module is relatively long, so the capacitance value and the second capacitance value corresponding to each fingerprint peak point in the first capacitance value are The size of the capacitance value corresponding to each fingerprint valley point may be greatly different.
  • a three-dimensional surface of unevenness may be formed, and then the The above three-dimensional surface simulates a fingerprint texture image.
  • the extraction module 10 may set a threshold range of the capacitance value according to the magnitude of each capacitance value in the acquired source fingerprint data, wherein the threshold range of the capacitance value may include more than 98% of the pixel points.
  • the extraction module 10 may extract, from the source fingerprint data obtained, the fingerprint data to be processed in which the fingerprint data value is within the preset threshold range, wherein the fingerprint data value may specifically be a fingerprint and each pixel of the fingerprint module.
  • the extraction module 10 removes the abnormal data by extracting the fingerprint data of the fingerprint data whose fingerprint data value is within a preset threshold range, and reduces the workload of the subsequent processing of the fingerprint data to be processed, thereby improving the efficiency of fingerprint recognition.
  • the processing module 20 may perform the foregoing fingerprint data to be processed.
  • the feature enlargement process enlarges the feature of the fingerprint data and enhances the recognizability of the fingerprint.
  • the to-be-processed fingerprint data may include the preset after the screening.
  • the first capacitance value and the second capacitance value in the threshold range wherein the first capacitance value is a capacitance value corresponding to a fingerprint peak point, and the second capacitance value is a capacitance value corresponding to the fingerprint valley point.
  • the processing module 20 may determine a median value of the first capacitance value and the second capacitance value according to the magnitudes of the respective capacitance values included in the first capacitance value and the second capacitance value of the to-be-processed fingerprint data, that is, the to-be-processed fingerprint data.
  • the median value may be set as the amplification reference value.
  • the amplifying reference value is used to process the to-be-processed fingerprint data into a series of data floating up and down around a median value, for enhancing the difference of features.
  • the fingerprint data to be processed may be subtracted from the above-mentioned enlarged reference value to obtain fingerprint data to be amplified, and then the above-mentioned to be enlarged
  • the processed fingerprint data is enlarged to highlight various fingerprint features.
  • the processing module 20 may multiply the fingerprint data to be enlarged by the specified coefficient, and then add the fingerprint data after the specified coefficient to the enlarged reference value to obtain the fingerprint data after the amplification process.
  • the specified coefficient is a multiple of the fingerprint feature, and the size of the specified coefficient may be determined according to an actual application scenario, and is not limited herein.
  • the processing module 20 adds the fingerprint data to be processed to the enlarged reference value to obtain the fingerprint data after the enlargement process, so that the feature difference of the fingerprint data is much larger than the feature difference of the fingerprint data before the enlargement.
  • the processing module 20 firstly subtracts the enlarged reference value from the fingerprint data to be processed, and then enlarges the fingerprint peak point and the fingerprint valley point in the fingerprint texture, and the fingerprint peak in the fingerprint texture.
  • the excessive texture of the dot and the fingerprint valley point (the pixel position corresponding to the median value of the capacitance) becomes a gray area, which increases the differentiation of the fingerprint texture. If the processing module 20 directly enlarges the fingerprint data to be processed, the fingerprint peak point, the fingerprint valley point, and the intermediate excessive texture are all enlarged, and the difference of the features cannot be highlighted, and thus the fingerprint data with clearer texture cannot be obtained.
  • the fingerprint data acquired by the extraction module 10 through the fingerprint module is presented. A blank area will appear in the fingerprint pattern, and if the fingerprint data is not repaired, the fingerprint will fail to be recognized.
  • the processing module 20 may divide all the pixel points of the fingerprint module into a plurality of pixel point regions, and each pixel dot region is a designated region, wherein each pixel region includes pixel points.
  • the number can be x*y, where x and y are both natural numbers, which can be determined according to the actual application scenario, and are not limited herein.
  • the processing module 20 After the processing module 20 obtains the capacitance value corresponding to the fingerprint peak point of each pixel point region and the capacitance value corresponding to the fingerprint valley point, the capacitance value corresponding to the fingerprint peak point and the capacitance value corresponding to the fingerprint valley point adjacent thereto may be determined. The difference between. If the fingerprint peak point of the certain pixel area and the maximum theoretical difference of the capacitance value corresponding to the fingerprint valley point are within the range of the maximum theoretical difference, the pixel area may be determined to be a normal fingerprint area, and the fingerprint data need not be repaired.
  • the maximum theoretical difference value may be determined in advance by a plurality of tests, that is, a maximum difference range between a capacitance value corresponding to a fingerprint peak point in a normal fingerprint texture determined by a plurality of tests and a capacitance value corresponding to a fingerprint valley point. There is no limit here.
  • the processing module 20 determines that the difference between the capacitance value corresponding to any fingerprint peak point in a certain pixel area and the capacitance value corresponding to the adjacent fingerprint valley point is greater than a preset difference threshold (ie, the above maximum theoretical difference) Scope), it can be determined that the above pixel area is an abnormal area, and the fingerprint data needs to be repaired.
  • a preset difference threshold ie, the above maximum theoretical difference
  • the processing module 20 repairs the fingerprint data of the abnormal area, the capacitance value corresponding to the fingerprint peak value of the preset difference value is greater than the capacitance value corresponding to the fingerprint peak point and the capacitance value corresponding to the fingerprint valley point, and then the fingerprint is deleted.
  • the peak point and the fingerprint valley point are added with corresponding capacitance values as the median value of the capacitance value corresponding to each pixel point in the pixel point region, so as to fill the texture of the fingerprint region where the abnormality occurs, and obtain the target fingerprint data of the complete fingerprint texture. .
  • the matching module 30 may generate a capacitor according to the capacitance value of each pixel included in the target fingerprint data. Three-dimensional surface. Since the capacitance value of each pixel is different, the three-dimensional surface generated according to the target fingerprint data will be an uneven three-dimensional surface, and the fingerprint texture image is simulated by the three-dimensional surface.
  • the matching module 30 simulates the fingerprint texture image through the three-dimensional surface, the simulated fingerprint texture image may be matched with the fingerprint verification data pre-stored in the mobile phone to determine whether the simulated fingerprint texture image and the fingerprint image presented by the fingerprint verification data are match.
  • the pre-stored fingerprint verification data is fingerprint data such as a fingerprint image that is registered by the user in advance and stored in a designated storage space of the mobile phone.
  • the matching module 30 determines the above-mentioned analog fingerprint texture image and the like.
  • the determining module 40 may determine that the fingerprint data is successfully identified, and thereby perform functions such as unlocking or waking up the mobile phone.
  • the mobile phone may first extract, from the source fingerprint data of the acquired fingerprint, each of the to-be-processed fingerprint data whose fingerprint data value is within a preset threshold range, and perform amplification processing and repairing the to-be-processed fingerprint data.
  • the target fingerprint data is processed, and then the fingerprint simulation image is generated according to the target fingerprint data, and the fingerprint verification data is matched with the fingerprint verification data such as the registered fingerprint image to determine whether the fingerprint recognition is successful. If the fingerprint recognition is successful, the corresponding mobile phone function can be performed.
  • the mobile phone obtains more complete target fingerprint data through the amplification and repair processing of the fingerprint data, which can reduce the workload of the late fingerprint data amplification processing and the repair processing, and reduce the power consumption of the fingerprint recognition.
  • the mobile phone generates simulated fingerprint data by amplifying processing and repairing the target fingerprint data, which can improve the accuracy of fingerprint recognition, improve the efficiency of fingerprint recognition, and thereby improve the applicability of fingerprint recognition and enhance the user experience of the terminal.
  • FIG. 3 is another schematic structural diagram of a terminal according to an embodiment of the present invention.
  • the terminal described in the embodiment of the present invention includes: a processor 1000 and a memory 2000, and the processor 1000 and the memory 2000 are connected by a bus 3000.
  • the above memory 2000 may be a high speed RAM memory or a non-volatile memory such as a disk memory.
  • the memory 2000 is used to store a set of program codes, and the processor 1000 is used to call the program code stored in the memory 2000 to perform the following operations:
  • fingerprint simulation data and the fingerprint verification data match successfully, it is determined that the source fingerprint data is successfully identified.
  • the processor 1000 is specifically configured to:
  • the first capacitance value and the second capacitance value are set as source fingerprint data for forming an analog fingerprint.
  • the to-be-processed fingerprint data is the first capacitance value and the second capacitance value within the preset threshold range
  • the processor 1000 is specifically configured to:
  • the processor 1000 is specifically configured to:
  • the capacitance value corresponding to any fingerprint peak point and the capacitance value corresponding to the fingerprint valley point adjacent to the fingerprint data is greater than a preset difference threshold, the capacitance value corresponding to the fingerprint peak point and The capacitance value corresponding to the fingerprint valley point is replaced by the median value of the capacitance value corresponding to each pixel point in the specified area.
  • the processor 1000 is specifically configured to:
  • the terminal described in the embodiment of the present invention may perform the implementation manner described in the embodiment of the method for fingerprint identification provided by the embodiment of the present invention by using the built-in modules (the memory 2000 and the processor 1000, etc.).
  • the implementations of the embodiments of the terminal provided by the embodiments of the present invention may also be implemented.
  • the embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium can store a program, and the program includes some or all of the steps of the fingerprint identification method described in the foregoing method embodiments.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

Abstract

本发明实施例公开了一种指纹识别的方法，包括：获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据；对各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据；根据所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配；若所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。本发明实施例还公开了一种终端。采用本发明，具有可降低指纹识别的难度，提高指纹识别的效率，增强终端的用户体验的优点。

Description

一种指纹识别的方法及终端
本发明要求2016年3月10日递交的发明名称为“一种指纹识别的方法及终端”的申请号201610137655.2的在先申请优先权，上述在先申请的内容以引入的方式并入本文本中。
技术领域
本发明涉及通信技术领域，尤其涉及一种指纹识别的方法及终端。
背景技术
当前随着手机等终端设备的技术发展的日益成熟，指纹识别技术成为了主流终端厂商旗舰机型的标配之一。指纹识别技术不仅可以用于终端的唤醒或者解锁等功能，也是移动支付的重要环节之一。指纹识别技术中指纹识别过程包括：指纹特征数据提取、指纹特征数据保存和指纹匹配等过程。
发明内容
本发明实施例提供一种指纹识别的方法及终端，可降低指纹识别的难度，提高指纹识别的效率，增强终端的用户体验。
本发明实施例提供了一种指纹识别的方法，其可包括：
获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据；
对各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据；
根据所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配；
若所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的指纹识别的方法的流程示意图；
图2是本发明实施例提供的终端的一结构示意图；
图3是本发明实施例提供的终端的另一结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
具体实现中，本发明实施例中所描述的终端可包括智能手机、平板电脑、个人数字助理(Personal Digital Assistant，PDA)、移动互联网设备(Mobile Internet Device，MID)、智能穿戴设备(如智能手表、智能手环)等各类设备，本发明实施例不作限定。下面将以手机为例对本发明实施例提供的指纹识别的方法及终端进行具体描述。
现有技术中，指纹识别时需要首先提取指纹特征数据，再对指纹特征数据进行初步处理得到更加清晰的指纹特征数据，进而通过将处理得到的指纹特征数据与预存的指纹模板进行特征点匹配。处理得到的指纹特征数据与预存的指纹模板匹配成功则可完成指纹识别，进而可执行终端的唤醒或者解锁等操作。现有技术对指纹特征数据进行提取得到指纹图像，对指纹图像进行处理得到清晰的图像，并未对图像中的指纹特征进行处理，处理方式简单。指纹匹配时通过指纹图像和指纹模板图像进行图像匹配来识别，由于指纹数据处理仅是图片处理，并未对指纹特征进行处理，无法确保获取的指纹特征的完整性，指纹匹配的识别出错率大，匹配效率低。
针对上述现有技术，参见图1，图1是本发明实施例提供的指纹识别的 方法的实施例流程示意图。本发明实施例中所描述的方法，包括步骤：
S101，获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据。
在一些可行的实施方式中，指纹识别可包括指纹的总体特征识别和指纹的局部特征识别等。其中，上述指纹的总体特征指那些人眼直接可以观察到的特征，包括：基本纹路图案，例如环形纹路、弓形纹路或者螺旋形纹路等。指纹的局部特征指指纹纹路的断点、分叉点或者转折点等节点特征，指纹的局部特征提供了指纹唯一性的确认节点特性。
在一些可行的实施方式中，手机可通过其内置的指纹模组获取用户手指的指纹数据。其中，上述指纹模组由指纹芯片构成，指纹芯片内部由包含m*n个队列似的像素点组成，其中，m和n为自然数。具体实现中，当手机检测到用户的手指按压在上述指纹模组表面时，可获取上述指纹模组表面的图像获取队列中各个像素点对应的电容值。其中，上述图像获取队列即为上述有m*n个像素点组成的像素点队列。用户手指按压在指纹模组表面时，手指与各个像素点之间形成电容，各个像素点对应的电容值会因为指纹的纹路中的指纹峰点和指纹谷点的差异而变化。手机可获取各个像素点与指纹纹路中的各个指纹峰点之间形成的各个电容的电容值，其中，一个像素点与一个指纹峰点之间形成的电容具有一个电容值。由于手指指纹的指纹峰点具有多个，每个指纹峰点对应一个电容值，手机可将获取到的各个指纹峰点对应的各个电容值设定为第一电容值。进一步的，手机还可获取各个像素点与指纹纹路中的指纹谷点之间形成的各个电容的电容值，其中，一个像素点与一个指纹谷点之间形成的电容具有一个电容值。由于手指指纹的指纹谷点具有多个，每个指纹谷点对应一个电容值，手机可将获取到的各个指纹谷点对应的各个电容值设定为第二电容值。
具体实现中，手机获取得到第一电容值和第二电容值之后，则可将上述第一电容值和第二电容值设定为用于形成模拟指纹的源指纹数据，以根据上述源指纹数据进行指纹匹配、识别。其中，上述源指纹数据的表现形式即为指纹纹路。由于指纹峰点和指纹模组的像素点的距离比较近，指纹谷点和指纹模组的像素点的距离比较远，因此第一电容值中各个指纹峰点对应的电容值和第二电容值中各个指纹谷点对应的电容值的大小会有较大的差异，手机的指纹模组 根据上述第一电容值和第二电容值形成模拟指纹时可形成一个凹凸不平的三维面，进而可通过上述三维面来模拟指纹纹路图像。
在一些可行的实施方式中，由于指纹模组表面的图像获取队列中包含的像素点中可能出现坏点，使得指纹和指纹模组接触不良，进而使得指纹数据中出现异常数据。具体实现中，手机可根据获取到的源指纹数据中各个电容值的大小状况，设定一个电容值的阈值范围，其中，上述电容值的阈值范围可囊括98％以上的像素点。手机可从上述获取的源指纹数据中提取指纹数据值在上述预设的阈值范围内的各项待处理指纹数据，其中，上述指纹数据值具体可为指纹与上述指纹模组的各个像素点形成的电容值。通过从源指纹数据中提取指纹数据值在预设的阈值范围内的待处理指纹数据来剔除异常数据，减少待处理指纹数据的后续处理的工作量，进而可提高指纹识别的效率。
S102，对各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据。
在一些可行的实施方式中，手机从源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据之后，则可对上述各项待处理指纹数据进行特征放大处理，将指纹数据的特征放大，增强指纹的可识别度。其中，上述指纹数据的特征放大具体表现形式可为指纹纹路的放大。具体实现中，上述待处理指纹数据可包括经过筛选后的在上述预设的阈值范围内的第一电容值和第二电容值，其中，上述第一电容值为指纹峰点对应的电容值，第二电容值为指纹谷点对应的电容值。手机可根据上述待处理指纹数据中第一电容值和第二电容值包含的各个电容值的大小，确定第一电容值和第二电容值的中位值，即，上述待处理指纹数据中各个电容值的50分位值。手机确定了上述第一电容值和第二电容值的中位值之后，则可将上述中位值设定为放大基准值。其中，上述放大基准值用于将上述待处理指纹数据处理为围绕中位值上下浮动的一系列数据，用于强化特征的差异性。
在一些可行的实施方式中，手机设定了上述放大基准值之后，则可上述各项待处理指纹数据减去上述放大基准值以得到待放大处理的指纹数据，进而可将上述待放大处理的指纹数据进行放大处理，突出各项指纹特征。具体实现中，手机可将待放大处理的指纹数据乘于指定系数，再将乘于指定系数之后的 各项指纹数据加上上述放大基准值，得到放大处理之后的指纹数据。其中，上述指定系数即为指纹特征的放大倍数，上述指定系数的大小具体可根据实际应用场景确定，在此不做限制。手机将待处理指纹数据进行放大处理之后再加回上述放大基准值以得到放大处理之后的指纹数据，使得指纹数据的特征差异远大于放大之前的指纹数据的特征差异。
需要说明的是，本发明实施例首先将待处理指纹数据减去放大基准值之后再进行放大，可使得指纹纹路中的指纹峰点和指纹谷点更加清晰，指纹纹路中的指纹峰点和指纹谷点的过度纹路(电容的中位值对应的像素点位置)变为灰色区域，加大了指纹纹路的差异化。若直接将待处理指纹数据进行放大，则将使得指纹峰点、指纹谷点以及中间过度纹路都进行放大了，无法突出特征的差异，进而无法获取纹路更加清晰的指纹数据。
在一些可行的实施方式中，当手指在手机的指纹模组上按下指纹时，若指纹模组的表面有脏东西等障碍物，则指纹模组获取到的指纹数据呈现出来的指纹纹路中将出现空白区域，此时若不对指纹数据进行修补，指纹将识别失败。具体实现中，手机获取得到放大处理得到的指纹数据之后，可各个像素点区域内的指纹数据进行检测，确定是否为正常纹路的指纹数据。具体的，手机可将指纹模组的所有像素点划分为多个像素点区域，每个像素点区域为一个指定区域，其中每个像素点区域包含的像素点个数可为x*y，其中，x和y均为自然数，具体可根据实际应用场景确定，在此不做限制。手机将所有像素点划分为多个像素点区域之后，则可获取每个像素点区域中包含的各个像素点的电容值，其中，各个像素点的电容值包括指纹峰点对应的电容值和指纹谷点对应的电容值。手机获取得到各个像素点区域的指纹峰点对应的电容值和指纹谷点对应的电容值之后，则可确定上述指纹峰点对应的电容值与与其相邻的指纹谷点对应的电容值之间的差值。若上述某一像素点区域的指纹峰点和指纹谷点对应的电容值的差值的最大理论差值范围内，则可确定该像素点区域为正常的指纹区域，无需进行指纹数据的修补。其中，上述最大理论差值具体可通过多次试验预先确定，即，通过多次试验确定的正常指纹纹路中指纹峰点对应的电容值和指纹谷点对应的电容值之间的最大差值范围，在此不做限制。
若某一像素点区域中出现任一指纹峰点对应的电容值与与其相邻的指纹 谷点对应的电容值的差值大于预设的差值阈值(即上述最大理论差值范围)，则可确定上述像素点区域为异常区域，需要进行指纹数据的修补处理。手机对异常区域的指纹数据进行修补时，可将上述电容值的差值大于预设的差值阈值的指纹峰点对应的电容值以及指纹谷点对应的电容值删除，再为上述指纹峰点和指纹谷点添加其对应的电容值为该像素点区域内各个像素点对应的电容值的中位值，以将出现异常的指纹区域的纹路填充完整，得到完整指纹纹路的目标指纹数据。
S103，根据所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配。
在一些可行的实施方式中，手机处理得到放大和修补处理之后的具有完整指纹纹路的目标指纹数据之后，则可根据上述目标指纹数据中包含的各个像素点的电容值生成一个三维面。由于每个像素点的电容值大小不同，因此根据上述目标指纹数据生成的三维面将会是一个凹凸不平的三维面，通过上述三维面来模拟指纹纹路图像。手机通过三维面来模拟指纹纹路图像之后，则可将上述模拟指纹纹路图像与手机中预存的指纹校验数据进行匹配，确定上述模拟指纹纹路图像是否与指纹校验数据呈现的指纹图像相匹配。其中，上述预存的指纹校验数据为用户预先通过手机注册并存储在手机的指定存储空间的指纹图像等指纹数据。
S104，若所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
在一些可行的实施方式中，手机确定上述模拟指纹纹路图像等指纹模拟数据与注册指纹图像等指纹校验数据匹配成功时，可确定上述指纹数据识别成功，进而可执行手机的解锁或者唤醒等功能。
在本发明实施例中，手机可首先从获取的指纹识别的源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据，对上述待处理指纹数据进行放大处理和修补处理得到目标指纹数据，进而根据目标指纹数据生成指纹模拟图像，通过指纹模拟图像与注册指纹图像等指纹校验数据进行匹配来确定指纹识别是否成功。若指纹识别成功则可执行相应的手机功能。本发明实施例手机通过指纹数据的放大和修补处理来得到更加完整的目标指纹数据，可减 少后期指纹数据放大处理和修补处理的工作量，降低指纹识别的功耗。手机通过放大处理和修补处理得到的目标指纹数据来生成模拟指纹数据，可提高指纹识别的准确性，提高指纹识别的效率，进而可提高指纹识别的适用性，增强终端的用户体验。
参见图2，图2是本发明实施例提供的终端的一结构示意图。本发明实施例中所描述的终端，包括：
提取模块10，用于获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据。
处理模块20，用于对所述提取模块获取的各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据。
匹配模块30，用于根据所述处理模块处理得到的所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配。
确定模块40，用于在所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
在一些可行的实施方式中，上述提取模块10具体用于：
在检测到手指按压在指纹模组表面时，获取所述指纹模组表面的图像获取队列中各个像素点与手指的指纹峰点之间形成的电容的第一电容值，以及所述指纹模组表面的图像获取队列中各个像素点与手指的指纹谷点之间形成的电容的第二电容值；
将所述第一电容值和所述第二电容值设定为用于形成模拟指纹的源指纹数据。
在一些可行的实施方式中，所述待处理指纹数据为在所述预设的阈值范围内的所述第一电容值和所述第二电容值；
上述处理模块20，具体用于：
获取所述第一电容值和所述第二电容值的中位值，并将所述中位值设定为放大基准值；
将各项所述待处理指纹数据减去所述放大基准值以得到待放大处理的指 纹数据；
将所述待放大处理的指纹数据乘于指定系数，再将乘于所述指定系数之后的各项指纹数据加上所述放大基准值以得到放大处理之后的指纹数据。
在一些可行的实施方式中，上述处理模块20具体用于：
获取所述放大处理得到的指纹数据中指定区域内的每个指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值；
当所述指纹数据中任一指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值大于预设的差值阈值时，将所述指纹峰点对应的电容值以及所述指纹谷点对应的电容值替换为所述指定区域内的各个像素点对应的电容值的中位值。
在一些可行的实施方式中，上述匹配模块30具体用于：
根据所述目标指纹数据中包含的各个像素点的电容值生成一个三维面，通过所述三维面模拟指纹纹路图像，以通过所述模拟指纹纹路图像进行指纹匹配。
在一些可行的实施方式中，本发明实施例中所描述的指纹识别可包括指纹的总体特征识别和指纹的局部特征识别等。其中，上述指纹的总体特征指那些人眼直接可以观察到的特征，包括：基本纹路图案，例如环形纹路、弓形纹路或者螺旋形纹路等。指纹的局部特征指指纹纹路的断点、分叉点或者转折点等节点特征，指纹的局部特征提供了指纹唯一性的确认节点特性。
在一些可行的实施方式中，手机的提取模块10可通过手机内置的指纹模组获取用户手指的指纹数据。其中，上述指纹模组由指纹芯片构成，指纹芯片内部由包含m*n个像素点，其中，上述m*n个像素点呈队列似排布，其中，m和n为自然数。具体实现中，当提取模块10检测到用户的手指按压在上述指纹模组表面时，可获取上述指纹模组表面的图像获取队列中各个像素点对应的电容值。其中，上述图像获取队列即为上述有m*n个像素点组成的像素点队列。用户手指按压在指纹模组表面时，手指与各个像素点之间形成电容，各个像素点对应的电容值会因为指纹的纹路中的指纹峰点和指纹谷点的差异而变化。提取模块10可获取各个像素点与指纹纹路中的各个指纹峰点之间形成的各个电容的电容值，其中，一个像素点与一个指纹峰点之间形成的电容具有 一个电容值。由于手指指纹的指纹峰点具有多个，每个指纹峰点对应一个电容值，提取模块10可将获取到的各个指纹峰点对应的各个电容值设定为第一电容值。进一步的，提取模块10还可获取各个像素点与指纹纹路中的指纹谷点之间形成的各个电容的电容值，其中，一个像素点与一个指纹谷点之间形成的电容具有一个电容值。由于手指指纹的指纹谷点具有多个，每个指纹谷点对应一个电容值，提取模块10可将获取到的各个指纹谷点对应的各个电容值设定为第二电容值。
具体实现中，提取模块10获取得到第一电容值和第二电容值之后，则可将上述第一电容值和第二电容值设定为用于形成模拟指纹的源指纹数据，以根据上述源指纹数据进行指纹匹配、识别。由于指纹峰点和指纹模组的像素点的距离比较近，指纹谷点和指纹模组的像素点的距离比较远，因此第一电容值中各个指纹峰点对应的电容值和第二电容值中各个指纹谷点对应的电容值的大小会有较大的差异，手机的指纹模组根据上述第一电容值和第二电容值形成模拟指纹时可形成一个凹凸不平的三维面，进而可通过上述三维面来模拟指纹纹路图像。
在一些可行的实施方式中，由于指纹模组表面的图像获取队列中包含的像素点中可能出现坏点，使得指纹和指纹模组接触不良，进而使得指纹数据中出现异常数据。具体实现中，提取模块10可根据获取到的源指纹数据中各个电容值的大小状况，设定一个电容值的阈值范围，其中，上述电容值的阈值范围可囊括98％以上的像素点。提取模块10可从上述获取的源指纹数据中提取指纹数据值在上述预设的阈值范围内的各项待处理指纹数据，其中，上述指纹数据值具体可为指纹与上述指纹模组的各个像素点形成的电容值。提取模块10通过从源指纹数据中提取指纹数据值在预设的阈值范围内的待处理指纹数据来剔除异常数据，减少待处理指纹数据的后续处理的工作量，进而可提高指纹识别的效率。
在一些可行的实施方式中，提取模块10从源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据之后，处理模块20则可对上述各项待处理指纹数据进行特征放大处理，将指纹数据的特征放大，增强指纹的可识别度。具体实现中，上述待处理指纹数据可包括经过筛选后的在上述预设的 阈值范围内的第一电容值和第二电容值，其中，上述第一电容值为指纹峰点对应的电容值，第二电容值为指纹谷点对应的电容值。处理模块20可根据上述待处理指纹数据中第一电容值和第二电容值包含的各个电容值的大小，确定第一电容值和第二电容值的中位值，即，上述待处理指纹数据中各个电容值的50分位值。处理模块20确定了上述第一电容值和第二电容值的中位值之后，则可将上述中位值设定为放大基准值。其中，上述放大基准值用于将上述待处理指纹数据处理为围绕中位值上下浮动的一系列数据，用于强化特征的差异性。
在一些可行的实施方式中，处理模块20设定了上述放大基准值之后，则可上述各项待处理指纹数据减去上述放大基准值以得到待放大处理的指纹数据，进而可将上述待放大处理的指纹数据进行放大处理，突出各项指纹特征。具体实现中，处理模块20可将待放大处理的指纹数据乘于指定系数，再将乘于指定系数之后的各项指纹数据加上上述放大基准值，得到放大处理之后的指纹数据。其中，上述指定系数即为指纹特征的放大倍数，上述指定系数的大小具体可根据实际应用场景确定，在此不做限制。处理模块20将待处理指纹数据进行放大处理之后再加回上述放大基准值以得到放大处理之后的指纹数据，使得指纹数据的特征差异远大于放大之前的指纹数据的特征差异。
需要说明的是，本发明实施例处理模块20首先将待处理指纹数据减去放大基准值之后再进行放大，可使得指纹纹路中的指纹峰点和指纹谷点更加清晰，指纹纹路中的指纹峰点和指纹谷点的过度纹路(电容的中位值对应的像素点位置)变为灰色区域，加大了指纹纹路的差异化。若处理模块20直接将待处理指纹数据进行放大，则将使得指纹峰点、指纹谷点以及中间过度纹路都进行放大了，无法突出特征的差异，进而无法获取纹路更加清晰的指纹数据。
在一些可行的实施方式中，当手指在手机的指纹模组上按下指纹时，若指纹模组的表面有脏东西等障碍物，则提取模块10通过指纹模组获取到的指纹数据呈现出来的指纹纹路中将出现空白区域，此时若不对指纹数据进行修补，指纹将识别失败。具体实现中，处理模块20获取得到放大处理得到的指纹数据之后，可各个像素点区域内的指纹数据进行检测，确定是否为正常纹路的指纹数据。具体的，处理模块20可将指纹模组的所有像素点划分为多个像素点区域，每个像素点区域为一个指定区域，其中每个像素点区域包含的像素点个 数可为x*y，其中，x和y均为自然数，具体可根据实际应用场景确定，在此不做限制。处理模块20将所有像素点划分为多个像素点区域之后，可获取每个像素点区域中包含的各个像素点的电容值，其中，各个像素点的电容值包括指纹峰点对应的电容值和指纹谷点对应的电容值。处理模块20获取得到各个像素点区域的指纹峰点对应的电容值和指纹谷点对应的电容值之后，则可确定上述指纹峰点对应的电容值与与其相邻的指纹谷点对应的电容值之间的差值。若上述某一像素点区域的指纹峰点和指纹谷点对应的电容值的差值的最大理论差值范围内，则可确定该像素点区域为正常的指纹区域，无需进行指纹数据的修补。其中，上述最大理论差值具体可通过多次试验预先确定，即，通过多次试验确定的正常指纹纹路中指纹峰点对应的电容值和指纹谷点对应的电容值之间的最大差值范围，在此不做限制。
若处理模块20确定某一像素点区域中出现任一指纹峰点对应的电容值与与其相邻的指纹谷点对应的电容值的差值大于预设的差值阈值(即上述最大理论差值范围)，则可确定上述像素点区域为异常区域，需要进行指纹数据的修补处理。处理模块20对异常区域的指纹数据进行修补时，可将上述电容值的差值大于预设的差值阈值的指纹峰点对应的电容值以及指纹谷点对应的电容值删除，再为上述指纹峰点和指纹谷点添加其对应的电容值为该像素点区域内各个像素点对应的电容值的中位值，以将出现异常的指纹区域的纹路填充完整，得到完整指纹纹路的目标指纹数据。
在一些可行的实施方式中，处理模块20处理得到放大和修补处理之后的具有完整指纹纹路的目标指纹数据之后，匹配模块30则可根据上述目标指纹数据中包含的各个像素点的电容值生成一个三维面。由于每个像素点的电容值大小不同，因此根据上述目标指纹数据生成的三维面将会是一个凹凸不平的三维面，通过上述三维面来模拟指纹纹路图像。匹配模块30通过三维面来模拟指纹纹路图像之后，则可将上述模拟指纹纹路图像与手机中预存的指纹校验数据进行匹配，确定上述模拟指纹纹路图像是否与指纹校验数据呈现的指纹图像相匹配。其中，上述预存的指纹校验数据为用户预先通过手机注册并存储在手机的指定存储空间的指纹图像等指纹数据。
在一些可行的实施方式中，匹配模块30确定上述模拟指纹纹路图像等指 纹模拟数据与注册指纹图像等指纹校验数据匹配成功时，确定模块40可确定上述指纹数据识别成功，进而可执行手机的解锁或者唤醒等功能。
在本发明实施例中，手机可首先从获取的指纹识别的源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据，对上述待处理指纹数据进行放大处理和修补处理得到目标指纹数据，进而根据目标指纹数据生成指纹模拟图像，通过指纹模拟图像与注册指纹图像等指纹校验数据进行匹配来确定指纹识别是否成功。若指纹识别成功则可执行相应的手机功能。本发明实施例手机通过指纹数据的放大和修补处理来得到更加完整的目标指纹数据，可减少后期指纹数据放大处理和修补处理的工作量，降低指纹识别的功耗。手机通过放大处理和修补处理得到的目标指纹数据来生成模拟指纹数据，可提高指纹识别的准确性，提高指纹识别的效率，进而可提高指纹识别的适用性，增强终端的用户体验。
参见图3，图3是本发明实施例提供的终端的另一结构示意图。本发明实施例中所描述的终端，包括：处理器1000和存储器2000，处理器1000和存储器2000通过总线3000连接。
上述存储器2000可以是高速RAM存储器，也可为非不稳定的存储器(non-volatile memory)，例如磁盘存储器。
其中，上述存储器2000用于存储一组程序代码，上述处理器1000用于调用存储器2000中存储的程序代码，执行如下操作：
获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据；
对各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据；
根据所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配；
若所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
在一些可行的实施方式中，上述处理器1000具体用于：
当检测到手指按压在指纹模组表面时，获取所述指纹模组表面的图像获取队列中各个像素点与手指的指纹峰点之间形成的电容的第一电容值，以及所述指纹模组表面的图像获取队列中各个像素点与手指的指纹谷点之间形成的电容的第二电容值；
将所述第一电容值和所述第二电容值设定为用于形成模拟指纹的源指纹数据。
在一些可行的实施方式中，所述待处理指纹数据为在所述预设的阈值范围内的所述第一电容值和所述第二电容值；
上述处理器1000具体用于：
获取所述第一电容值和所述第二电容值的中位值，并将所述中位值设定为放大基准值；
将各项所述待处理指纹数据减去所述放大基准值以得到待放大处理的指纹数据；
将所述待放大处理的指纹数据乘于指定系数，再将乘于所述指定系数之后的各项指纹数据加上所述放大基准值以得到放大处理之后的指纹数据。
在一些可行的实施方式中，上述处理器1000具体用于：
获取所述放大处理得到的指纹数据中指定区域内的每个指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值；
当所述指纹数据中任一指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值大于预设的差值阈值时，将所述指纹峰点对应的电容值以及所述指纹谷点对应的电容值替换为所述指定区域内的各个像素点对应的电容值的中位值。
在一些可行的实施方式中，上述处理器1000具体用于：
根据所述目标指纹数据中包含的各个像素点的电容值生成一个三维面，通过所述三维面模拟指纹纹路图像，以通过所述模拟指纹纹路图像进行指纹匹配。
具体实现中，本发明实施例中所描述的终端可通过其内置的各个模块(存储器2000和处理器1000等)执行本发明实施例提供的指纹识别的方法的实施例中所描述的实现方式，也可执行本发明实施例提供的终端的实施例所描述的实现方式，具体实现过程可参见上述各个实施例，在此不再赘述。
本发明实施例还提供一种计算机存储介质，其中，该计算机存储介质可存储有程序，该程序执行时包括上述方法实施例中记载的任何一种指纹识别的方法的部分或全部步骤。
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流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述的存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)或随机存储记忆体(Random Access Memory，RAM)等。
以上所揭露的仅为本发明较佳实施例而已，当然不能以此来限定本发明之权利范围，因此依本发明权利要求所作的等同变化，仍属本发明所涵盖的范围。

Claims (20)

  1. 一种指纹识别的方法，其特征在于，包括：
    获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据；
    对各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据；
    根据所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配；
    若所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
  2. 如权利要求1所述的方法，其特征在于，所述获取指纹识别的源指纹数据，包括：
    当检测到手指按压在指纹模组表面时，获取所述指纹模组表面的图像获取队列中各个像素点与手指的指纹峰点之间形成的电容的第一电容值，以及所述指纹模组表面的图像获取队列中各个像素点与手指的指纹谷点之间形成的电容的第二电容值；
    将所述第一电容值和所述第二电容值设定为用于形成模拟指纹的源指纹数据。
  3. 如权利要求2所述的方法，其特征在于，所述待处理指纹数据为在所述预设的阈值范围内的所述第一电容值和所述第二电容值；
    所述对各项所述待处理指纹数据进行特征放大处理，包括：
    获取所述第一电容值和所述第二电容值的中位值，并将所述中位值设定为放大基准值；
    将各项所述待处理指纹数据减去所述放大基准值以得到待放大处理的指纹数据；
    将所述待放大处理的指纹数据乘于指定系数，再将乘于所述指定系数之后的各项指纹数据加上所述放大基准值以得到放大处理之后的指纹数据。
  4. 如权利要求3所述的方法，其特征在于，所述对放大处理得到的指纹 数据进行修补，包括：
    获取所述放大处理得到的指纹数据中指定区域内的每个指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值；
    当所述指纹数据中任一指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值大于预设的差值阈值时，将所述指纹峰点对应的电容值以及所述指纹谷点对应的电容值替换为所述指定区域内的各个像素点对应的电容值的中位值。
  5. 如权利要求2-4任一项所述的方法，其特征在于，所述根据所述目标指纹数据生成指纹模拟数据，包括：
    根据所述目标指纹数据中包含的各个像素点的电容值生成一个三维面，通过所述三维面模拟指纹纹路图像，以通过所述模拟指纹纹路图像进行指纹匹配。
  6. 一种终端，其特征在于，包括：
    提取模块，用于获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据；
    处理模块，用于对所述提取模块获取的各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据；
    匹配模块，用于根据所述处理模块处理得到的所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配；
    确定模块，用于在所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
  7. 如权利要求6所述的终端，其特征在于，所述提取模块具体用于：
    在检测到手指按压在指纹模组表面时，获取所述指纹模组表面的图像获取队列中各个像素点与手指的指纹峰点之间形成的电容的第一电容值，以及所述指纹模组表面的图像获取队列中各个像素点与手指的指纹谷点之间形成的电容的第二电容值；
    将所述第一电容值和所述第二电容值设定为用于形成模拟指纹的源指纹数据。
  8. 如权利要求7所述的终端，其特征在于，所述待处理指纹数据为在所述预设的阈值范围内的所述第一电容值和所述第二电容值；
    所述处理模块，具体用于：
    获取所述第一电容值和所述第二电容值的中位值，并将所述中位值设定为放大基准值；
    将各项所述待处理指纹数据减去所述放大基准值以得到待放大处理的指纹数据；
    将所述待放大处理的指纹数据乘于指定系数，再将乘于所述指定系数之后的各项指纹数据加上所述放大基准值以得到放大处理之后的指纹数据。
  9. 如权利要求8所述的终端，其特征在于，所述处理模块具体用于：
    获取所述放大处理得到的指纹数据中指定区域内的每个指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值；
    当所述指纹数据中任一指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值大于预设的差值阈值时，将所述指纹峰点对应的电容值以及所述指纹谷点对应的电容值替换为所述指定区域内的各个像素点对应的电容值的中位值。
  10. 如权利要求7-9任一项所述的终端，其特征在于，所述匹配模块具体用于：
    根据所述目标指纹数据中包含的各个像素点的电容值生成一个三维面，通过所述三维面模拟指纹纹路图像，以通过所述模拟指纹纹路图像进行指纹匹配。
  11. 一种终端，其特征在于，包括：存储器和处理器，所述存储器和所述处理器通过总线连接；
    所述存储器，用于存储一组程序代码；
    所述处理器，用于调用所述存储器中存储的程序代码执行以下操作：
    获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据；
    对各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据；
    根据所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配；
    若所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
  12. 如权利要求11所述的终端，其特征在于，在所述获取指纹识别的源指纹数据方面，所述处理器用于：
    当检测到手指按压在指纹模组表面时，获取所述指纹模组表面的图像获取队列中各个像素点与手指的指纹峰点之间形成的电容的第一电容值，以及所述指纹模组表面的图像获取队列中各个像素点与手指的指纹谷点之间形成的电容的第二电容值；
    将所述第一电容值和所述第二电容值设定为用于形成模拟指纹的源指纹数据。
  13. 如权利要求12所述的终端，其特征在于，所述待处理指纹数据为在所述预设的阈值范围内的所述第一电容值和所述第二电容值；
    在所述对各项所述待处理指纹数据进行特征放大处理方面，所述处理器用于：
    获取所述第一电容值和所述第二电容值的中位值，并将所述中位值设定为放大基准值；
    将各项所述待处理指纹数据减去所述放大基准值以得到待放大处理的指纹数据；
    将所述待放大处理的指纹数据乘于指定系数，再将乘于所述指定系数之后的各项指纹数据加上所述放大基准值以得到放大处理之后的指纹数据。
  14. 如权利要求13所述的终端，其特征在于，在所述对放大处理得到的指纹数据进行修补方面，所述处理器用于：
    获取所述放大处理得到的指纹数据中指定区域内的每个指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值；
    当所述指纹数据中任一指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值大于预设的差值阈值时，将所述指纹峰点对应的电容值以及所述指纹谷点对应的电容值替换为所述指定区域内的各个像素点对应的电 容值的中位值。
  15. 如权利要求12-14任一项所述的终端，其特征在于，在所述根据所述目标指纹数据生成指纹模拟数据方面，所述处理器用于：
    根据所述目标指纹数据中包含的各个像素点的电容值生成一个三维面，通过所述三维面模拟指纹纹路图像，以通过所述模拟指纹纹路图像进行指纹匹配。
  16. 一种计算机存储介质，其特征在于，所述计算机存储介质中存储有程序，所述程序执行时包括以下步骤：
    获取指纹识别的源指纹数据，并从所述源指纹数据中提取指纹数据值在预设的阈值范围内的各项待处理指纹数据；
    对各项所述待处理指纹数据进行特征放大处理，并对放大处理得到的指纹数据进行修补，以得到修补后的目标指纹数据；
    根据所述目标指纹数据生成指纹模拟数据，并将所述指纹模拟数据与预存的指纹校验数据进行匹配；
    若所述指纹模拟数据与所述指纹校验数据匹配成功，则确定所述源指纹数据识别成功。
  17. 如权利要求16所述的计算机存储介质，其特征在于，在所述获取指纹识别的源指纹数据方面，所述程序执行时包括以下步骤：
    当检测到手指按压在指纹模组表面时，获取所述指纹模组表面的图像获取队列中各个像素点与手指的指纹峰点之间形成的电容的第一电容值，以及所述指纹模组表面的图像获取队列中各个像素点与手指的指纹谷点之间形成的电容的第二电容值；
    将所述第一电容值和所述第二电容值设定为用于形成模拟指纹的源指纹数据。
  18. 如权利要求17所述的计算机存储介质，其特征在于，所述待处理指纹数据为在所述预设的阈值范围内的所述第一电容值和所述第二电容值；
    在所述对各项所述待处理指纹数据进行特征放大处理方面，所述程序执行时包括以下步骤：
    获取所述第一电容值和所述第二电容值的中位值，并将所述中位值设定为 放大基准值；
    将各项所述待处理指纹数据减去所述放大基准值以得到待放大处理的指纹数据；
    将所述待放大处理的指纹数据乘于指定系数，再将乘于所述指定系数之后的各项指纹数据加上所述放大基准值以得到放大处理之后的指纹数据。
  19. 如权利要求18所述的计算机存储介质，其特征在于，在所述对放大处理得到的指纹数据进行修补方面，所述程序执行时包括以下步骤：
    获取所述放大处理得到的指纹数据中指定区域内的每个指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值；
    当所述指纹数据中任一指纹峰点对应的电容值以及与其相邻的指纹谷点对应的电容值的差值大于预设的差值阈值时，将所述指纹峰点对应的电容值以及所述指纹谷点对应的电容值替换为所述指定区域内的各个像素点对应的电容值的中位值。
  20. 如权利要求17-19任一项所述的计算机存储介质，其特征在于，在所述根据所述目标指纹数据生成指纹模拟数据方面，所述程序执行时包括以下步骤：
    根据所述目标指纹数据中包含的各个像素点的电容值生成一个三维面，通过所述三维面模拟指纹纹路图像，以通过所述模拟指纹纹路图像进行指纹匹配。
PCT/CN2016/087778 2016-03-10 2016-06-29 一种指纹识别的方法及终端 WO2017152549A1 (zh)

Priority Applications (4)

Application Number Priority Date Filing Date Title
ES16893194T ES2763848T3 (es) 2016-03-10 2016-06-29 Procedimiento de identificación de huella dactilar y terminal
EP16893194.7A EP3296922B1 (en) 2016-03-10 2016-06-29 Fingerprint identification method and terminal
US15/846,236 US10572713B2 (en) 2016-03-10 2017-12-19 Fingerprint identification method and terminal
US15/988,928 US10169640B2 (en) 2016-03-10 2018-05-24 Method for fingerprint identification and terminal

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201610137655.2A CN105760851B (zh) 2016-03-10 2016-03-10 一种指纹识别的方法及终端
CN201610137655.2 2016-03-10

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US15/846,236 Continuation US10572713B2 (en) 2016-03-10 2017-12-19 Fingerprint identification method and terminal

Publications (1)

Publication Number Publication Date
WO2017152549A1 true WO2017152549A1 (zh) 2017-09-14

Family

ID=56331955

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/087778 WO2017152549A1 (zh) 2016-03-10 2016-06-29 一种指纹识别的方法及终端

Country Status (5)

Country Link
US (2) US10572713B2 (zh)
EP (1) EP3296922B1 (zh)
CN (1) CN105760851B (zh)
ES (1) ES2763848T3 (zh)
WO (1) WO2017152549A1 (zh)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105447437B (zh) * 2015-02-13 2017-05-03 比亚迪股份有限公司 指纹识别方法和装置
CN105760851B (zh) * 2016-03-10 2018-03-02 广东欧珀移动通信有限公司 一种指纹识别的方法及终端
US20190056810A1 (en) * 2017-08-17 2019-02-21 Novatek Microelectronics Corp. Method of Handling Sensing Data and Related Image Sensing Circuit
CN107644220B (zh) * 2017-09-30 2021-03-05 上海摩软通讯技术有限公司 移动终端的指纹识别方法及系统
US10783346B2 (en) * 2017-12-11 2020-09-22 Invensense, Inc. Enhancing quality of a fingerprint image
CN108171165B (zh) * 2017-12-28 2021-10-22 北京小米移动软件有限公司 指纹识别方法、装置及计算机可读存储介质
CN108197550A (zh) * 2017-12-28 2018-06-22 北京小米移动软件有限公司 故障提示方法、装置及电子设备
CN108595044B (zh) * 2018-03-29 2020-08-04 维沃移动通信有限公司 一种触摸屏的控制方法及终端
US11348364B2 (en) * 2018-12-18 2022-05-31 Booz Allen Hamilton Inc. Method and system for neural fingerprint enhancement for fingerprint recognition
CN114663928A (zh) * 2022-03-31 2022-06-24 上海天马微电子有限公司 指纹检测装置及指纹检测方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1480896A (zh) * 2002-09-04 2004-03-10 �����������������ͳ��ʶ������ 指纹识别方法，以及指纹控制方法和系统
CN101499130A (zh) * 2008-01-30 2009-08-05 深圳市普罗巴克科技股份有限公司 一种指纹识别方法和指纹识别系统
CN101790165A (zh) * 2009-01-23 2010-07-28 黄房富 一种手机指纹识别器及使用方法
US8379943B2 (en) * 2009-05-28 2013-02-19 Li-Kuo Chiu High-performance fingerprint image-processing method

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09161053A (ja) * 1995-12-13 1997-06-20 Mitsubishi Electric Corp 指紋照合装置
US7480397B2 (en) * 2003-04-18 2009-01-20 Casio Computer Co., Ltd. Fingerprint image reading apparatus
JP4440603B2 (ja) * 2003-11-06 2010-03-24 アルプス電気株式会社 容量検出回路及び検出方法並びにそれを用いた指紋センサ
WO2011046614A2 (en) * 2009-10-16 2011-04-21 The Regents Of The University Of California Methods and systems for phylogenetic analysis
JP2016513983A (ja) * 2013-02-06 2016-05-19 ソナベーション， インコーポレイテッド 指組織内に埋め込まれた皮下構造の３次元撮像のためのバイオメトリック感知デバイス
US10203816B2 (en) * 2013-05-07 2019-02-12 Egis Technology Inc. Apparatus and method for TFT fingerprint sensor
SE1351489A1 (sv) * 2013-12-12 2015-06-13 Fingerprint Cards Ab Fingeravtrycksavkänningssystem och -metod
US10503948B2 (en) * 2014-03-06 2019-12-10 Qualcomm Incorporated Multi-spectral ultrasonic imaging
CN105320918B (zh) * 2014-07-04 2019-01-22 映智科技股份有限公司 指纹传感器
US10437974B2 (en) * 2015-06-18 2019-10-08 Shenzhen GOODIX Technology Co., Ltd. Optical sensing performance of under-screen optical sensor module for on-screen fingerprint sensing
CN105094600A (zh) * 2015-06-29 2015-11-25 联想(北京)有限公司 一种信息处理方法及电子设备
CN105760851B (zh) * 2016-03-10 2018-03-02 广东欧珀移动通信有限公司 一种指纹识别的方法及终端

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1480896A (zh) * 2002-09-04 2004-03-10 �����������������ͳ��ʶ������ 指纹识别方法，以及指纹控制方法和系统
CN101499130A (zh) * 2008-01-30 2009-08-05 深圳市普罗巴克科技股份有限公司 一种指纹识别方法和指纹识别系统
CN101790165A (zh) * 2009-01-23 2010-07-28 黄房富 一种手机指纹识别器及使用方法
US8379943B2 (en) * 2009-05-28 2013-02-19 Li-Kuo Chiu High-performance fingerprint image-processing method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3296922A4 *

Also Published As

Publication number Publication date
EP3296922B1 (en) 2019-12-11
US10169640B2 (en) 2019-01-01
CN105760851B (zh) 2018-03-02
US10572713B2 (en) 2020-02-25
US20180268186A1 (en) 2018-09-20
ES2763848T3 (es) 2020-06-01
EP3296922A4 (en) 2018-09-05
EP3296922A1 (en) 2018-03-21
US20180107853A1 (en) 2018-04-19
CN105760851A (zh) 2016-07-13

Similar Documents

Publication Publication Date Title
WO2017152549A1 (zh) 一种指纹识别的方法及终端
TW202024997A (zh) 二維碼識別方法、裝置及設備
WO2020156034A1 (zh) 指纹录入方法及相关装置
TW202026992A (zh) 行業識別模型確定方法和裝置
CN104217194B (zh) 图像核对方法及装置、模型模板生成方法及装置
US10235548B2 (en) Method for setting fingerprint sensor interrupt threshold, apparatus, and terminal device
CN110969046B (zh) 人脸识别方法、设备及计算机可读存储介质
WO2020192532A1 (zh) 指纹图像处理方法及相关装置
CN111160288A (zh) 手势关键点检测方法、装置、计算机设备和存储介质
WO2020125062A1 (zh) 一种图像融合方法及相关装置
CN108898549A (zh) 图片处理方法、图片处理装置及终端设备
CN111640123B (zh) 无背景图像的生成方法、装置、设备及介质
CN115375582A (zh) 一种基于低阶泰勒分解的摩尔纹消解方法及装置
CN115496976A (zh) 多源异构数据融合的视觉处理方法、装置、设备及介质
CN117333928B (zh) 一种人脸特征点检测方法、装置、电子设备及存储介质
WO2018137226A1 (zh) 指纹提取方法及装置
JP2015026117A (ja) 画像処理方法、画像処理装置、プログラム及び記録媒体
CN110059645A (zh) 一种人脸识别方法、系统及电子设备和存储介质
CN114782692A (zh) 房屋模型的修复方法、装置、电子设备和可读存储介质
JP6175904B2 (ja) 照合対象抽出システム、照合対象抽出方法、照合対象抽出プログラム
CN110334667B (zh) 基于ircnn和mtcnn的具有尺度旋转不变性的静脉识别方法及系统
TWI816150B (zh) 目標對象檢測方法、電子設備及儲存介質
CN116912815A (zh) 一种文本提取方法、装置及电子设备
TW201807618A (zh) 部分指紋驗證的疊代匹配方法與系統
CN107194014B (zh) 数据源调用方法及装置

Legal Events

Date Code Title Description
WWE Wipo information: entry into national phase

Ref document number: 2016893194

Country of ref document: EP

NENP Non-entry into the national phase

Ref country code: DE