WO2017067291A1 - 一种指纹识别的方法、装置及终端 - Google Patents
一种指纹识别的方法、装置及终端 Download PDFInfo
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- WO2017067291A1 WO2017067291A1 PCT/CN2016/093750 CN2016093750W WO2017067291A1 WO 2017067291 A1 WO2017067291 A1 WO 2017067291A1 CN 2016093750 W CN2016093750 W CN 2016093750W WO 2017067291 A1 WO2017067291 A1 WO 2017067291A1
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- 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
Definitions
- Embodiments of the present invention relate to the field of fingerprint identification, and in particular, to a method, device, and terminal for fingerprint identification.
- the fingerprint recognition technology provides a more accurate and quick authentication method for the user, and avoids the occurrence of the situation that the user forgets to bring a magnetic card in the magnetic card authentication mode of the prior art or the magnetic card is demagnetized due to improper use.
- the existing fingerprint recognition technology includes two parts: registration and authentication. First, the user collects fingerprint image data of the target finger through the fingerprint sensor, and saves the fingerprint image data as preset image data. At this time, the user is on the fingerprint sensor. The registration is successful; then, when the user needs to use the fingerprint sensor for authentication, the fingerprint image data of the target finger is collected again by the fingerprint sensor, and the fingerprint sensor matches the collected fingerprint image data with the preset image data, if the matching If successful, the certification is passed.
- an embodiment of the present invention provides a method for fingerprint identification, the method comprising:
- Fingerprint recognition is performed according to the target fingerprint image data.
- the embodiment of the present invention further provides a device for fingerprint identification, the device comprising:
- An acquisition module configured to continuously perform at least two fingerprint image acquisitions on the target finger, and obtain at least two pieces of initial fingerprint image data of the target finger;
- a calculation module configured to calculate an average value of pixel values of corresponding pixel points of each piece of the initial fingerprint image data, and map the average value to corresponding pixel points to form target fingerprint image data;
- an identification module configured to perform fingerprint identification according to the target fingerprint image data.
- an embodiment of the present invention further provides a terminal, including a memory and a processor, where the memory stores instructions executable by a processor, and the processor is configured to execute an instruction in the memory, where the instruction is used by Do the following:
- Fingerprint recognition is performed according to the target fingerprint image data.
- the embodiment of the invention provides a method, a device and a terminal for fingerprint recognition, so as to achieve the purpose of improving the recognition rate of the fingerprint sensor.
- FIG. 1 is a flowchart of a method for fingerprint identification according to Embodiment 1 of the present invention
- FIG. 2 is a flowchart of a method for fingerprint identification according to Embodiment 2 of the present invention.
- FIG. 3 is a schematic structural diagram of a device for fingerprint recognition according to Embodiment 3 of the present invention.
- FIG. 4 is a schematic structural diagram of a terminal according to Embodiment 4 of the present invention.
- the embodiment of the invention provides a fingerprint identification method, which comprises: continuously performing at least two fingerprint image acquisitions on a target finger, acquiring at least two initial fingerprint image data of the target finger; and calculating each initial fingerprint image.
- the data corresponds to an average value of pixel values of the pixel points, and the average value is mapped to corresponding pixel points to constitute target fingerprint image data; and fingerprint identification is performed according to the target fingerprint image data.
- the calculating an average value of pixel values corresponding to the pixel points of each piece of the initial fingerprint image data includes: acquiring coordinate values of pixel points in each piece of initial fingerprint image data; determining all initials according to the coordinate values a pixel point located at the same position in the fingerprint image data, and calculating a sum of pixel values of the pixel points located at the same position; calculating an average value of the pixel values of the corresponding pixel points according to the sum of the pixel values.
- the performing fingerprint identification according to the target image data includes: extracting a feature point to be detected of the target finger fingerprint in the target fingerprint image data; and performing the feature point to be detected and the preset fingerprint feature point Match; confirm that the target finger is a safe finger when the match passes.
- the matching the feature point to be detected with the preset fingerprint feature point comprises: matching the feature points to be detected one by one based on the preset fingerprint feature points; determining the to-be-waited when the matching is passed
- the feature point is a security feature point; the number of the security feature points is calculated; and the target finger is confirmed to be a secure finger when the matching is passed, specifically: when the number of the security feature points is greater than or equal to a preset safety quantity When it is confirmed, the target finger is a safe finger.
- the method further includes: extracting a feature point to be detected in the target fingerprint image data; and performing the feature to be detected based on the preset fingerprint feature point Matching to determine a first feature point of the feature point to be detected; saving the first feature point in the preset fingerprint feature point to update the preset fingerprint feature point;
- the first feature point is that the parameters of the feature point to be tested do not match the feature points that are successful.
- the method for fingerprint identification before the matching the feature point to be detected and the preset fingerprint feature point, further includes: extracting a to-be-detected texture of the target finger fingerprint in the target fingerprint image data; The method for matching the texture to be detected is matched with the preset pattern; if the matching is successful, the operation of matching the feature point to be tested with the preset fingerprint feature point is performed.
- the parameter of the feature point to be detected includes a coordinate value and a type value, wherein the type of the feature point to be detected includes a center point, a bifurcation point, a termination point, and a triangle point.
- An embodiment of the present invention further provides an apparatus for fingerprint identification, comprising: an acquisition module, configured to continuously perform at least two fingerprint image acquisitions on a target finger, and obtain at least two initial fingerprint image data of the target finger; and a calculation module, And an average value of the pixel values of the corresponding pixel points of the initial fingerprint image data, and the average value is mapped to the corresponding pixel points to form the target fingerprint image data; and the identification module is configured to use the target fingerprint image data according to the data Perform fingerprint recognition.
- the calculating module is specifically configured to: acquire coordinate values of pixel points in each piece of initial fingerprint image data; determine pixel points located at the same position in all initial fingerprint image data according to the coordinate values, and calculate the same The sum of the pixel values of the pixel points of the position; the average value of the pixel values of the corresponding pixel points is calculated according to the sum of the pixel values.
- the identification module includes: an extracting unit, configured to extract a feature point to be detected of the target finger fingerprint in the target fingerprint image data; and a matching unit, configured to use the feature point to be detected and the preset fingerprint feature The point is matched; the confirmation unit is configured to confirm that the target finger is a secure finger when the match passes.
- the matching unit includes: a matching sub-unit for matching the to-be-checked feature points one by one based on the preset fingerprint feature points; and a security feature point confirmation sub-unit, configured to determine the The feature point to be checked is a security feature point; the quantity calculation subunit is configured to calculate the number of the security feature points; and the confirmation unit is specifically configured to: when the number of the security feature points is greater than or equal to a preset safety quantity , confirm that the target finger is a safe finger.
- the apparatus for fingerprinting further includes: an extracting module, configured to extract a feature point to be detected in the target fingerprint image data after confirming that the target finger is a secure finger when the matching passes; the first feature a point confirmation module, configured to match the to-be-checked feature points based on the preset fingerprint feature points to determine a first feature point of the to-be-checked feature points; and an update module, configured to save the first feature points In the preset fingerprint feature point, the preset fingerprint feature point is updated; wherein the first feature point is that the parameter of the feature point to be tested does not match the successful feature point.
- the identifying module is further configured to: before the matching the feature point to be detected and the preset fingerprint feature point, extract the to-be-detected texture of the target finger fingerprint in the target fingerprint image data; The pattern to be detected is matched with the preset pattern; if the matching is successful, the operation of matching the feature point to be tested with the preset fingerprint feature point is performed.
- the parameter of the feature point to be detected includes a coordinate value and a type value, wherein the type of the feature point to be detected includes a center point, a bifurcation point, a termination point, and a triangle point.
- An embodiment of the present invention further provides a terminal, including a memory and a processor, where the memory stores instructions executable by the processor, the processor is configured to execute an instruction in the memory, where the instruction is used to execute the following Operation: performing at least two fingerprint image acquisitions on the target finger continuously, acquiring at least two pieces of initial fingerprint image data of the target finger; calculating an average value of pixel values of corresponding pixel points of each piece of the initial fingerprint image data, The average value is mapped to the corresponding pixel to form the target fingerprint image data; and the fingerprint is performed according to the target fingerprint image data.
- the instructions are further configured to: obtain coordinate values of pixel points in each piece of initial fingerprint image data; determine pixel points at the same position in all initial fingerprint image data according to the coordinate values, and Calculating a sum of pixel values of pixel points located at the same position; calculating an average value of pixel values of the corresponding pixel points according to the sum of the pixel values.
- the instruction is further configured to: extract a feature point to be detected of the target finger fingerprint in the target fingerprint image data; match the to-be-checked feature point with the preset fingerprint feature point; When passing, it is confirmed that the target finger is a safe finger.
- the instructions are further configured to: perform the following operations on the feature points to be detected one by one based on the preset fingerprint feature points; and determine that the feature points to be detected are security feature points when the matching passes; Determining the number of security feature points; determining that the target finger is a secure finger when the matching is passed, specifically: when the number of the security feature points is greater than or equal to a preset security number, confirming that the target finger is a secure finger .
- the instructions are further configured to: extract feature points to be detected in the target fingerprint image data; and match the to-be-checked feature points based on preset fingerprint feature points to determine the to-be-checked Detecting a first feature point in the feature point; storing the first feature point in the preset fingerprint feature point to update the preset fingerprint feature point; wherein the first feature point is a feature to be detected The parameters of the point do not match the successful feature points.
- the parameter of the feature point to be detected includes a coordinate value and a type value, wherein the type of the feature point to be detected includes a center point, a bifurcation point, a termination point, and a triangle point.
- FIG. 1 is a flowchart of a method for fingerprint identification according to Embodiment 1 of the present invention.
- the method can be performed by a device for fingerprint recognition, wherein the device can be implemented by software and/or hardware, and is generally integrated in a fingerprint recognition sensor, the fingerprint recognition sensor can be integrated in a mobile terminal, and the mobile terminal can include a smart phone, Devices such as tablets.
- the method specifically includes the following operations:
- S101 Perform at least two fingerprint image acquisitions on the target finger continuously, and obtain at least two pieces of initial fingerprint image data of the target finger.
- the fingerprint sensor constructs a Cartesian coordinate system with a certain vertex of each acquired fingerprint image as a coordinate origin, and obtains coordinate values of different pixel points.
- the coordinate value of different pixel points is determined by taking the upper left corner of the fingerprint image collected as the coordinate origin.
- the coordinates of each pixel in the first fingerprint image are A1 (x1, y1), B1 (x2, y2), C1 (x3, y3), etc., second.
- the coordinates of each pixel in the secondary fingerprint image are A2 (x1, y1), B2 (x2, y2), C2 (x3, y3), etc.
- the coordinates of each pixel in the third fingerprint image are A3 (x1, Y1), B3 (x2, y2), C3 (x3, y3), and the like.
- the pixel values of the corresponding pixel points in each piece of initial fingerprint image data are determined using the coordinate values.
- the advantage of acquiring the initial fingerprint image data of the plurality of target fingers is that the average value of the pixel values of the corresponding pixel points is calculated by using the initial fingerprint image data of the plurality of target fingers, thereby eliminating the noise existing in the fingerprint image acquisition. Improve the recognition rate of fingerprints in subsequent operations, making the identification of fingerprint sensors more accurate.
- the fingerprint sensor collects the target finger fingerprint image continuously, and the continuous acquisition time is in the millisecond level, that is, compared with the time when the current fingerprint sensor collects the target finger fingerprint image, the present invention is implemented.
- the user cannot perceive whether the fingerprint sensor has performed multiple acquisitions or one acquisition.
- the number of times of the collection may be manually set or may be set by default. In the embodiment of the present invention, the number of times of collection is not specifically limited, and may be collected twice or more times. Those skilled in the art should know that the more the number of target finger fingerprint images are collected, the closer the average value of the pixel values of the corresponding pixel points calculated by subsequent operations is to the true value, but the time required for the calculation also increases correspondingly.
- the number of acquisitions can be flexibly set by the scene of the fingerprint sensor application or the needs of the user.
- Different pixel points in each target fingerprint image data are determined by corresponding coordinate values.
- the process of calculating the average value of the pixel values of the corresponding pixel points of each piece of initial fingerprint image data is: first, the pixel value of the pixel at the same position in each target fingerprint image data is determined by the coordinate value, and the calculation is performed. The sum of the pixel values of the initial fingerprint image data at the position, and then the average of the pixel values of the position is calculated using the sum of the pixel values. For example, three consecutive fingerprint image acquisitions are performed on the target finger to obtain initial fingerprint image data of three target fingers. In the Cartesian coordinate system, the pixel values of the pixel points at a certain coordinate position are respectively M1, M2 and M3, and the calculation is performed.
- the calculation method of the average value of the pixel values of the pixels at other positions in the initial fingerprint image data is the same as above, and will not be described herein.
- the average of the pixel values of all the pixel points is calculated and mapped to the corresponding pixel, which constitutes the target fingerprint image data.
- the to-be-detected texture of the target finger may be extracted from the target fingerprint image data, and then the to-be-detected texture is matched with the preset texture, and the fingerprint identification of the target finger is successfully confirmed by the matching. Success; or,
- the target pattern and the feature point to be detected of the target finger may also be extracted from the target fingerprint image data, and the pattern to be detected is first matched with the preset pattern. After the matching is successful, the feature point to be inspected and the preset feature point are further selected. The template is matched, and the matching succeeds to confirm that the fingerprint recognition of the target finger is successful.
- the advantage of the setting is that the extraction time of the target fingerprint path is shorter and the recognition is more convenient than the extraction and recognition of the target finger fingerprint feature point. Firstly, the fingerprint of the target finger is roughly identified by using the extracted texture to be detected, and if the recognition fails, the user is directly prompted to identify the failure, which saves the recognition time and improves the working efficiency of the fingerprint sensor; or
- the feature point of the target finger can be directly extracted from the target fingerprint image data, and the feature point to be tested is directly matched with the preset feature point, and the matching is successful to confirm that the fingerprint recognition of the target finger is successful.
- multiple fingerprint images of the target finger are acquired to obtain initial fingerprint image data of the plurality of target fingers; and then an average value of the pixel values of the corresponding pixel points in the plurality of initial fingerprint image data is calculated, and the average value is obtained. Mapping to corresponding pixel points to constitute target fingerprint image data.
- the method improves the recognition rate of the fingerprint image by using the mean noise reduction method, and solves the problem that the fingerprint image recognition rate is low due to the noise generated by the fingerprint sensor when collecting the target finger fingerprint image data, thereby improving the user experience.
- FIG. 2 is a flowchart of a fingerprint identification method according to Embodiment 2 of the present invention.
- the fingerprint identification method provided by Embodiment 2 of the present invention is based on the technical solution of the foregoing embodiment.
- the method includes the following operations:
- S201 Perform at least two fingerprint image acquisitions on the target finger continuously, and acquire at least two pieces of initial fingerprint image data of the target finger.
- S202 Calculate an average value of pixel values of corresponding pixel points of each piece of the initial fingerprint image data, and map the average value to corresponding pixel points to form target fingerprint image data.
- a fingerprint is a pattern formed by a convex or concave skin of the human body. Since the fingerprint of a person is unique, that is, each person's fingerprint has its own unique distinction and other individual characteristics, the fingerprints observed by the naked eye are characterized in that the fingerprint includes a bucket pattern, and the fingerprint is composed of a plurality of concentric circles. Or a threaded line; including a bow line, this fingerprint is arranged like a bow. These fingerprints observed by the naked eye are not continuous, smooth and straight, but often interrupted, bifurcated or turned. These break points, bifurcation points or turning points are the feature points of the fingerprint. In addition, the characteristics of the fingerprint Points can also include some divergence points, isolated points or ring points.
- the fingerprint sensor recognizes the fingerprint mainly by identifying the feature point of the fingerprint.
- the fingerprint sensor does not directly save the fingerprint image of the collected finger, but extracts the finger. The feature point of the fingerprint image and save the feature point for continued use.
- extracting the feature points of the fingerprint is a very important and critical operation for fingerprint recognition.
- the fingerprint feature points in the target fingerprint image data of the target finger of the user are extracted by using an algorithm, and the extracted feature points are referred to as feature points to be tested.
- the number and/or parameter value of the feature point to be detected of the target finger fingerprint extracted from the target fingerprint image data may be different each time, for example, because the target fingerprint image data obtained by each acquisition is different, so that The feature points to be detected extracted from the target fingerprint image data are also not identical.
- the parameter of the feature point to be detected may include a coordinate value and a type value of the feature point, wherein the coordinate value indicates the position of the feature point, and the type of the feature point includes a center point, a bifurcation point, a termination point, and a triangle point, for example, a feature
- the coordinates of point A are (x, y) and the type is a bifurcation point.
- the feature points of the fingerprint may further include other parameter values, for example, may include a direction parameter indicating that the feature point is oriented in a certain direction, or a curvature parameter indicating a speed of changing the direction of the feature point, and the like.
- a direction parameter indicating that the feature point is oriented in a certain direction
- a curvature parameter indicating a speed of changing the direction of the feature point
- the preset fingerprint feature point is a feature point of extracting a fingerprint of a specific finger in the fingerprint image data processed by the user in the case that the fingerprint sensor first recognizes a specific finger of a certain user, and saves it to form Preset fingerprint feature point template.
- the function of the preset fingerprint feature point template is to identify the fingerprint of the specific finger of the corresponding user in the subsequent operation.
- the feature points in the preset fingerprint feature point template are only the feature points of the finger extracted based on the processed fingerprint image data obtained by the first recognition, and may not include all the feature points of the finger. .
- the feature point to be detected is matched with the preset fingerprint feature point.
- the matching may be based on the parameter values of the feature points to be inspected.
- the matching is performed according to the coordinate value and the type value of the feature point to be inspected. That is, it is possible to confirm whether there is a feature point with the same coordinate value of the feature point to be detected in the preset fingerprint feature point, and if so, determine whether the type value of the preset fingerprint feature point is the same as the type value of the feature point to be tested, If it is the same, it is confirmed that the feature points to be checked match and pass.
- the smart phone in the standby state can directly work, or can enter the application interface of the smart phone corresponding to the target finger according to the collected target finger. Wait.
- the fingerprint sensor prompts the user to continue fingerprint collection. If the fingerprint collection cannot be passed after the specified number of times, the user is prompted to enter the password to authenticate; or, the prompt User fingerprint collection has exceeded the specified number of times. Please re-collect after the specified time.
- step S204 and step S205 it can be further implemented by the following scheme:
- the target finger is a secure finger.
- the feature points to be inspected are matched with the preset fingerprint feature point templates one by one, and then the feature points to be inspected are divided into three types, one is the parameter of the feature point to be inspected and the preset fingerprint feature point template in the above matching process.
- the parameter matching of a certain feature point is successful.
- the feature point to be detected is confirmed as a security feature point;
- the second is the parameter of the feature point to be inspected in the above matching process and the feature point in the preset fingerprint feature point template.
- a certain parameter is not successfully matched.
- the feature point to be tested does not contribute to the matching of the target finger, and the feature point to be inspected is directly discarded.
- the third type is that the preset fingerprint feature point template does not exist in the above matching process.
- the feature point corresponding to the parameter value of the feature point to be inspected, that is, the parameter of the feature point to be tested is not successfully matched. At this time, the feature point to be tested whose feature point parameters are not matched successfully is determined as the first feature point. Used to further update the preset feature point template in subsequent operations.
- the feature point parameter to be detected is taken as a coordinate value and a type value as an example.
- the coordinate value of a certain feature point A to be inspected is (x, y), and the type value is a bifurcation point.
- the coordinate value and the type value match successfully, it is confirmed that the feature point to be tested is a security feature point; if only the coordinate value or the type value matches successfully, the to-be-checked feature is discarded. If the parameter value and the type value of the feature point to be tested are not matched successfully, it is confirmed that the feature point to be detected is the first feature point, and is used to update the preset fingerprint feature point template in the subsequent operation.
- the fingerprint identification device may Confirm that the target finger is a safe finger.
- the smart phone enters the working state from the standby state, or further, the smart phone can directly enter the main interface of the application matching the finger, for example, when When the fingerprint recognition device recognizes that the security finger is the index finger of the user, the fingerprint recognition device enters the corresponding text message editing main interface, and when the fingerprint recognition device recognizes that the security finger is the user's middle finger, enters the corresponding webpage browsing main interface. When the fingerprint recognition device recognizes that the security finger is the thumb of the user, the fingerprint recognition device enters a corresponding photographing main interface or the like.
- the advantage of this setting is that after the smart phone enters the working state from the standby state, the user needs to perform the operation of selecting the common application again, on the one hand, saving the operation time of the user and improving the user experience; On the one hand, it reduces the operation of the equipment and prolongs the standby time and service life of the equipment.
- the preset safety quantity setting may be an artificial preset or a fingerprint sensor device default setting.
- the fingerprint sensor can accurately recognize the finger. Therefore, when setting the preset safety quantity, the number of feature points can be set by 50; or it can be flexibly set according to the working scene of the fingerprint sensor or the user's needs.
- the feature point to be detected extracted by the processed fingerprint image data of the target finger is not exactly the same as the feature point in the preset feature point template, and it is possible that the number of the feature points to be detected is smaller than the preset feature.
- the number of points, or it is also possible that the number of feature points to be inspected is greater than or equal to the number of preset feature points.
- the processed fingerprint image data of the target finger acquired at this time includes more feature points about the target finger fingerprint, and at this time, The extra unmatched successful feature points to be tested are referred to as first feature points.
- the first feature point is saved in the preset fingerprint feature point to update the preset fingerprint feature point.
- the first feature point is that the parameters of the feature point to be tested are not successfully matched. Feature points.
- the first feature point is added to the preset feature point template on the basis of the preset feature point, so that the feature point about the user target finger in the preset feature point template is more comprehensive.
- the identification of the fingerprint sensor in subsequent operations is more accurate.
- the foregoing manner is only one way to update the preset feature point template.
- the preset feature point template may be updated in other manners, for example, all the features to be detected may also be The point replaces the feature point in the preset feature point template to update the preset feature point template.
- steps S206-S208 are further added technical solutions after step S205 on the basis of the technical solutions of the above embodiments.
- steps S201-S205 can be implemented separately or simultaneously with steps S206-S208.
- multiple fingerprint images of the target finger are acquired to obtain initial fingerprint image data of the plurality of target fingers; and then an average value of the pixel values of the corresponding pixel points in the plurality of initial fingerprint image data is calculated, and the average value is obtained. Mapping to corresponding pixel points to constitute target fingerprint image data. Then, using the feature points of the fingerprint extracted from the target fingerprint image data, the fingerprint of the target finger is matched, and the matching is passed to confirm that the target finger is a secure finger. Moreover, after confirming that it is a secure finger, the preset fingerprint feature point template may be further updated according to the extracted fingerprint feature points, and the method uses the mean noise reduction method to improve the recognition rate of the fingerprint image, and solves the fingerprint problem. The problem that the fingerprint image recognition rate caused by the noise generated by the sensor when collecting the target finger fingerprint image data is low, and the user experience is improved.
- FIG. 3 is a schematic structural diagram of an apparatus for fingerprint identification according to Embodiment 3 of the present invention.
- the embodiment of the fingerprint identification device is implemented based on the embodiment of the method for fingerprint identification described above.
- the device for fingerprint identification reference may be made to the embodiment of the method for fingerprint identification.
- the device includes the following modules: an acquisition module 31, a calculation module 32, and an identification module 33;
- the acquiring module 31 is configured to continuously perform at least two fingerprint image acquisitions on the target finger, and acquire at least two pieces of initial fingerprint image data of the target finger;
- the calculation module 32 is configured to calculate an average value of pixel values of corresponding pixel points of each piece of the initial fingerprint image data, and map the average value to corresponding pixel points to form target fingerprint image data;
- the identification module 33 is configured to perform fingerprint identification according to the target fingerprint image data.
- the fingerprint module is acquired by the acquisition module 31 to obtain the initial fingerprint image data of the plurality of target fingers, and then the calculation module 32 calculates the pixel values of the corresponding pixel points of the plurality of initial fingerprint image data.
- the average value is used as the target fingerprint image data as the pixel value of the pixel, and the fingerprint of the target finger is recognized by the recognition module 33.
- the embodiment of the invention improves the recognition rate of the fingerprint image by using the mean noise reduction method, and solves the problem that the fingerprint image recognition rate is low due to the noise generated by the fingerprint sensor when collecting the target finger fingerprint image data, thereby improving the user experience. .
- the identification module 33 includes:
- the extracting unit 331 is configured to extract a feature point to be detected of the target finger fingerprint in the target fingerprint image data
- the matching unit 332 is configured to match the to-be-checked feature point with the preset fingerprint feature point
- the confirmation unit 333 is configured to confirm that the target finger is a safe finger when the matching is passed.
- the matching unit 332 includes:
- a security feature point confirmation sub-unit 3322 configured to determine that the feature point to be inspected is a security feature point when the matching is passed;
- the confirmation unit 333 is specifically configured to:
- the target finger is a secure finger.
- the device further includes:
- the extracting module 34 is configured to extract a feature point to be detected in the target fingerprint image data after confirming that the target finger is a secure finger when the matching is passed;
- the first feature point confirmation module 35 is configured to match the to-be-checked feature points based on the preset fingerprint feature points to determine a first feature point of the to-be-checked feature points;
- the update module 36 is configured to save the first feature point in the preset fingerprint feature point to update the preset fingerprint feature point; wherein the first feature point is the feature point to be tested None of the parameters match the successful feature points.
- the parameter of the feature point to be detected includes a coordinate value and a type value, wherein the type of the feature point to be detected includes a center point, a bifurcation point, a termination point, and a triangle point.
- the embodiment of the present invention further provides a terminal.
- the terminal may include radio frequency (RF, Radio).
- Circuit 401 memory 402 including one or more computer readable storage media, input unit 403, display unit 404, sensor 405, audio circuit 406, wireless fidelity (WiFi, Wireless)
- the Fidelity module 407 includes a processor 408 having one or more processing cores, and a power supply 409 and the like. It will be understood by those skilled in the art that the terminal structure shown in FIG. 4 does not constitute a limitation to the terminal, and may include more or less components than those illustrated, or a combination of certain components, or different component arrangements. among them:
- the RF circuit 401 can be used for transmitting and receiving information or during a call, and receiving and transmitting signals. Specifically, after receiving downlink information of the base station, the downlink information is processed by one or more processors 408. In addition, the data related to the uplink is sent to the base station. .
- RF circuitry 401 includes, but is not limited to, an antenna, at least one amplifier, a tuner, one or more oscillators, a subscriber identity module (SIM, Subscriber Identity Module) Card, Transceiver, Coupler, Low Noise Amplifier (LNA, Low Noise) Amplifier), duplexer, etc.
- SIM Subscriber identity module
- LNA Low Noise Amplifier
- the RF circuit 401 can also communicate with the network and other devices through wireless communication.
- the wireless communication can use any communication standard or protocol, including but not limited to a global mobile communication system (GSM, Global System of Mobile communication), General Packet Radio Service (GPRS, General Packet Radio) Service), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA, Wideband Code) Division Multiple Access), Long Term Evolution (LTE), e-mail, short message service (SMS, Short) Messaging Service) and so on.
- GSM Global System of Mobile communication
- GPRS General Packet Radio Service
- GPRS General Packet Radio Service
- CDMA Code Division Multiple Access
- WCDMA Wideband Code Division Multiple Access
- LTE Long Term Evolution
- SMS Short message service
- the memory 402 can be used to store software programs and modules, and the processor 408 executes various functional applications and data processing by running software programs and modules stored in the memory 402.
- the memory 402 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored according to Data created by the use of the terminal (such as audio data, phone book, etc.).
- memory 402 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, memory 402 may also include a memory controller to provide access to memory 402 by processor 408 and input unit 403.
- Input unit 403 can be used to receive input numeric or character information, as well as to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.
- input unit 403 can include a touch-sensitive surface as well as other input devices.
- Touch-sensitive surfaces also known as touch screens or trackpads, collect touch operations on or near the user (such as the user using a finger, stylus, etc., any suitable object or accessory on a touch-sensitive surface or touch-sensitive Operation near the surface), and drive the corresponding connecting device according to a preset program.
- the touch sensitive surface may include two parts of a touch detection device and a touch controller.
- the touch detection device detects the touch orientation of the user, and detects a signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into contact coordinates, and sends the touch information.
- the processor 408 is provided and can receive commands from the processor 408 and execute them.
- touch-sensitive surfaces can be implemented in a variety of types, including resistive, capacitive, infrared, and surface acoustic waves.
- the input unit 403 can also include other input devices. Specifically, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, joysticks, and the like.
- Display unit 404 can be used to display information entered by the user or information provided to the user, as well as various graphical user interfaces of the terminal, which can be composed of graphics, text, icons, video, and any combination thereof.
- the display unit 404 can include a display panel, and optionally, a liquid crystal display (LCD, Liquid) can be used. Crystal Display), Organic Light-Emitting (OLED) Diode) and other forms to configure the display panel.
- the touch-sensitive surface can cover the display panel, and when the touch-sensitive surface detects a touch operation thereon or nearby, it is transmitted to the processor 408 to determine the type of the touch event, and then the processor 408 displays the type according to the type of the touch event. A corresponding visual output is provided on the panel.
- the touch-sensitive surface and display panel are implemented as two separate components to perform input and input functions, in some embodiments, the touch-sensitive surface can be integrated with the display panel to implement input and output functions.
- the terminal may also include at least one type of sensor 405, such as a light sensor, motion sensor, and other sensors.
- the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel according to the brightness of the ambient light, and the proximity sensor may close the display panel and/or the backlight when the terminal moves to the ear.
- the gravity acceleration sensor can detect the magnitude of acceleration in all directions (usually three axes). When it is stationary, it can detect the magnitude and direction of gravity.
- gesture of the mobile phone such as horizontal and vertical screen switching, related Game, magnetometer attitude calibration), vibration recognition related functions (such as pedometer, tapping), etc.;
- Other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, and the like that can be configured in the terminal are not described herein.
- the audio circuit 406, the speaker, and the microphone provide an audio interface between the user and the terminal.
- the audio circuit 406 can transmit the converted electrical signal of the audio data to the speaker, and convert it into a sound signal output by the speaker; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 406 and then converted.
- the audio data is then processed by the audio data output processor 408, sent via RF circuitry 401 to, for example, another terminal, or the audio data is output to memory 402 for further processing.
- the audio circuit 406 may also include an earbud jack to provide communication between the peripheral earphone and the terminal.
- WiFi is a short-range wireless transmission technology
- the terminal can help users to send and receive emails, browse web pages, and access streaming media through the WiFi module 407, which provides wireless broadband Internet access for users.
- FIG. 4 shows the WiFi module 407, it can be understood that it does not belong to the necessary configuration of the terminal, and can be omitted as needed within the scope of not changing the essence of the invention.
- Processor 408 is the control center of the terminal, which connects various portions of the entire handset using various interfaces and lines, by executing or executing software programs and/or modules stored in memory 402, and by invoking data stored in memory 402, The various functions of the terminal and processing data to monitor the mobile phone as a whole.
- the processor 408 may include one or more processing cores; preferably, the processor 408 may integrate an application processor and a modem processor, where the application processor mainly processes an operating system, a user interface, an application, and the like.
- the modem processor primarily handles wireless communications. It will be appreciated that the above described modem processor may also not be integrated into the processor 408.
- the terminal also includes a power source 409 (such as a battery) that supplies power to the various components.
- the power source can be logically coupled to the processor 408 through a power management system to manage functions such as charging, discharging, and power management through the power management system.
- the power supply 409 may also include any one or more of a DC or AC power source, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
- the terminal may further include a camera, a Bluetooth module, and the like, and details are not described herein again.
- the processor 408 in the terminal loads the executable file corresponding to the process of one or more applications into the memory 402 according to the following instruction, and is executed by the processor 408 to be stored in the memory.
- the application in 402 to implement various functions:
- Fingerprint recognition is performed according to the target fingerprint image data.
- the terminal can implement the effective effect of the device for the fingerprint recognition provided by the embodiment of the present invention.
- the terminal can implement the effective effect of the device for the fingerprint recognition provided by the embodiment of the present invention.
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Abstract
本发明实施例公开了一种指纹识别的方法、装置及终端，该方法包括：连续对目标手指进行至少两次指纹图像采集，获取至少两份目标手指的初始指纹图像数据；计算每份初始指纹图像数据对应像素点的像素值的平均值，将平均值映射到对应的像素点构成目标指纹图像数据，根据目标指纹图像数据进行指纹识别。
Description
本申请要求于2015年10 月19
日提交中国专利局、申请号为201510679989.8、发明名称为“一种指纹识别的方法和装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
本发明实施例涉及指纹识别领域，尤其涉及一种指纹识别的方法、装置及终端。
随着科学的进步和发展，安全认证的方式越来越多，其中，利用指纹进行安全认证的方式越来越受到用户的关注。
目前，指纹识别技术为用户提供了一种更加准确且快捷的认证方式，避免了现有技术的磁卡认证方式中用户忘带磁卡或者由于使用不当导致磁卡消磁而无法实现认证等情况的出现。现有的指纹识别技术包括注册和认证两部分，首先，用户通过指纹传感器采集其目标手指的指纹图像数据，并将该指纹图像数据作为预设图像数据进行保存，此时用户在此指纹传感器上注册成功；然后，在用户需要利用该指纹传感器进行认证时，再次通过该指纹传感器采集其目标手指的指纹图像数据，并且指纹传感器将采集得到的指纹图像数据与预设图像数据进行匹配，若匹配成功，则认证通过。
然而，现有技术中采集得到的指纹图像数据中通常会存在一些噪点，这些噪点的存在使得指纹图像的识别率降低，给用户的认证造成麻烦，降低了用户的使用体验。
一方面，本发明实施例提供了一种指纹识别的方法，该方法包括：
连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；
计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；
根据所述目标指纹图像数据进行指纹识别。
第二方面，本发明实施例还提供了一种指纹识别的装置，该装置包括：
采集模块，用于连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；
计算模块，用于计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；
识别模块，用于根据所述目标指纹图像数据进行指纹识别。
第三方面，本发明实施例还提供了一种终端，包括存储器和处理器，所述存储器存储有处理器可执行的指令，所述处理器用于执行所述存储器中的指令，所述指令用于执行如下操作：
连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；
计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；
根据所述目标指纹图像数据进行指纹识别。
有鉴于此，本发明实施例提供了一种指纹识别的方法、装置及终端，以达到提高指纹传感器的识别率的目的。
图1为本发明实施例一提供的一种指纹识别的方法的流程图；
图2为本发明实施例二提供的一种指纹识别的方法的流程图；
图3为本发明实施例三提供的一种指纹识别的装置的结构示意图；
图4为本发明实施例四提供的一种终端的结构示意图。
下面结合附图和实施例对本发明作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅仅用于解释本发明，而非对本发明的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与本发明相关的部分而非全部结构。
本发明实施例提供了一种指纹识别的方法，其包括：连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；根据所述目标指纹图像数据进行指纹识别。
一些实施例中，所述计算每份所述初始指纹图像数据对应像素点的像素值的平均值，包括：获取每份初始指纹图像数据中像素点的坐标值；根据所述坐标值确定所有初始指纹图像数据中位于相同位置的像素点，并计算位于相同位置的像素点的像素值之和；根据所述像素值之和计算对应像素点的像素值的平均值。
一些实施例中，所述根据所述目标图像数据进行指纹识别，包括：提取所述目标指纹图像数据中目标手指指纹的待检特征点；将所述待检特征点与预设指纹特征点进行匹配；在匹配通过时确认所述目标手指为安全手指。
一些实施例中，所述将所述待检特征点与预设指纹特征点进行匹配，包括：基于预设指纹特征点对所述待检特征点进行逐一匹配；在匹配通过时确定所述待检特征点为安全特征点；计算所述安全特征点的数量；所述在匹配通过时确认所述目标手指为安全手指，具体为：当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
一些实施例中，所述在匹配通过时确认所述目标手指为安全手指之后，还包括：提取所述目标指纹图像数据中的待检特征点；基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点；将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为待检特征点的参数均未匹配成功的特征点。
一些实施例中，在将所述待检特征点与预设指纹特征点进行匹配之前，所述指纹识别的方法还包括：提取所述目标指纹图像数据中目标手指指纹的待检纹路；将所述待检纹路与预设纹路进行匹配；若匹配成功，则执行将所述待检特征点与预设指纹特征点进行匹配的操作。
一些实施例中，所述待检特征点的参数包括坐标值和类型值，其中，所述待检特征点的类型包括中心点、分叉点、终止点和三角点。
本发明实施例还提供了一种指纹识别的装置，包括：采集模块，用于连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；计算模块，用于计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；识别模块，用于根据所述目标指纹图像数据进行指纹识别。
一些实施例中，所述计算模块具体用于：获取每份初始指纹图像数据中像素点的坐标值；根据所述坐标值确定所有初始指纹图像数据中位于相同位置的像素点，并计算位于相同位置的像素点的像素值之和；根据所述像素值之和计算对应像素点的像素值的平均值。
一些实施例中，所述识别模块包括：提取单元，用于提取所述目标指纹图像数据中目标手指指纹的待检特征点；匹配单元，用于将所述待检特征点与预设指纹特征点进行匹配；确认单元，用于在匹配通过时确认所述目标手指为安全手指。
一些实施例中，所述匹配单元包括：逐一匹配子单元，用于基于预设指纹特征点对所述待检特征点进行逐一匹配；安全特征点确认子单元，用于在匹配通过时确定所述待检特征点为安全特征点；数量计算子单元，用于计算所述安全特征点的数量；所述确认单元具体用于：当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
一些实施例中，所述指纹识别的装置还包括：提取模块，用于在匹配通过时确认所述目标手指为安全手指之后，提取所述目标指纹图像数据中的待检特征点；第一特征点确认模块，用于基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点；更新模块，用于将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为待检特征点的参数均未匹配成功的特征点。
一些实施例中，所述识别模块还用于：在将所述待检特征点与预设指纹特征点进行匹配之前，提取所述目标指纹图像数据中目标手指指纹的待检纹路；将所述待检纹路与预设纹路进行匹配；若匹配成功，则执行将所述待检特征点与预设指纹特征点进行匹配的操作。
一些实施例中，所述待检特征点的参数包括坐标值和类型值，其中，所述待检特征点的类型包括中心点、分叉点、终止点和三角点。
本发明实施例还提供了一种终端，其包括存储器和处理器，所述存储器存储有处理器可执行的指令，所述处理器用于执行所述存储器中的指令，所述指令用于执行如下操作：连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；根据所述目标指纹图像数据进行指纹识别。
一些实施例中，所述指令还用于执行如下操作：：获取每份初始指纹图像数据中像素点的坐标值；根据所述坐标值确定所有初始指纹图像数据中位于相同位置的像素点，并计算位于相同位置的像素点的像素值之和；根据所述像素值之和计算对应像素点的像素值的平均值。
一些实施例中，所述指令还用于执行如下操作：提取所述目标指纹图像数据中目标手指指纹的待检特征点；将所述待检特征点与预设指纹特征点进行匹配；在匹配通过时确认所述目标手指为安全手指。
一些实施例中，所述指令还用于执行如下操作：基于预设指纹特征点对所述待检特征点进行逐一匹配；在匹配通过时确定所述待检特征点为安全特征点；计算所述安全特征点的数量；所述在匹配通过时确认所述目标手指为安全手指，具体为：当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
一些实施例中，所述指令还用于执行如下操作：提取所述目标指纹图像数据中的待检特征点；基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点；将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为待检特征点的参数均未匹配成功的特征点。
一些实施例中，所述待检特征点的参数包括坐标值和类型值，其中，所述待检特征点的类型包括中心点、分叉点、终止点和三角点。
实施例一
图1为本发明实施例一提供的一种指纹识别的方法的流程图。该方法可以由指纹识别的装置执行，其中该装置可由软件和/或硬件实现，并一般集成在指纹识别传感器中，该指纹识别传感器可以集成在移动终端中，所述移动终端可以包括智能手机、平板电脑等设备。
参见图1，该方法具体包括如下操作：
S101、连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据。
在对手指的指纹图像进行采集的过程中，指纹传感器中大量的采集电路采集对应位置的手指的指纹图像。由于采集电路的精度和手指位置的相对性，使得采集得到的指纹图像上存在噪点，该噪点会影响后续操作中指纹传感器对用户指纹的识别。
上述操作中，连续对目标手指进行多次指纹图像采集，以获取多份该目标手指的初始指纹图像数据。在采集指纹图像时，无论目标手指的位置在采集区域如何变化，指纹传感器均以每次采集到的指纹图像的某一顶点为坐标原点构建直角坐标系，并以此得到不同像素点的坐标值。例如，在本发明实施例中，以每次采集到的指纹图像的左上角为坐标原点，确定不同像素点的坐标值。以连续对目标手指进行三次指纹图像采集为例，第一次的指纹图像中各像素点的坐标为A1（x1，y1），B1（x2，y2），C1（x3，y3）等，第二次的指纹图像中各像素点的坐标为A2（x1，y1），B2（x2，y2），C2（x3，y3）等，第三次的指纹图像中各像素点的坐标为A3（x1，y1），B3（x2，y2），C3（x3，y3）等。在后续操作中，利用坐标值确定每份初始指纹图像数据中对应的像素点的像素值。
另外，获取多份目标手指的初始指纹图像数据的好处是，利用多份目标手指的初始指纹图像数据计算得到对应像素点的像素值的平均值，以此消除在指纹图像采集中存在的噪点，提高后续操作中指纹的识别率，使得指纹传感器的识别更加精准。
在此，需要说明的是，指纹传感器对目标手指指纹图像的采集是连续进行的，连续采集的时间在毫秒级，即与目前的指纹传感器采集一次目标手指指纹图像的时间相比，本发明实施例中用户无法感知指纹传感器是进行了多次采集还是一次采集。另外，采集的次数可以人为预先设定或者也可以由程序默认设定，在本发明实施例中，对采集的次数不作具体限定，可以采集两次或者两次以上的任意多次。本领域技术人员应该知晓，当采集的目标手指指纹图像次数越多，后续操作计算得到的相应像素点的像素值的平均值越接近真实值，但同时计算所需的时间也相应增长，因此，采集的次数可由指纹传感器应用的场景或用户的需求灵活设定。
S102、计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据。
每份目标指纹图像数据中不同像素点由相应的坐标值确定。在本实施例中，计算每份初始指纹图像数据对应像素点的像素值的平均值的过程为，首先由坐标值确定每份目标指纹图像数据中相同位置的像素点的像素值，并计算多份初始指纹图像数据该位置的像素值之和，然后利用该像素值之和计算该位置的像素值的平均值。例如，连续对目标手指进行了三次指纹图像采集，得到三份目标手指的初始指纹图像数据，在直角坐标系中某一坐标位置的像素点的像素值分别为M1，M2和M3，计算得到该位置的像素值之和为M=M1+M2+M3，则可以得到该位置像素值的平均值为V=M/3。然后将该平均值V映射到该位置作为该位置像素点的像素值。
初始指纹图像数据中其他位置像素点的像素值的平均值的计算方法同上，在此不再赘述。计算得到所有位置像素点的像素值的平均值，并将其映射到对应的像素点，便构成了目标指纹图像数据。
S103、根据所述目标指纹图像数据进行指纹识别。
根据目标指纹图像数据对目标手指进行识别时，可以从目标指纹图像数据中提取该目标手指的待检纹路，然后将该待检纹路与预设纹路进行匹配，匹配成功确认该目标手指的指纹识别成功；或者，
还可以从该目标指纹图像数据中提取该目标手指的待检纹路和待检特征点，首先将待检纹路与预设纹路进行匹配，匹配成功后，进一步将待检特征点与预设特征点模板进行匹配，匹配成功确认该目标手指的指纹识别成功，这样设置的好处是，相对于目标手指指纹特征点的提取和识别，目标手指纹路的提取时间更短，识别更方便。首先利用提取到的待检纹路对目标手指的指纹进行粗识别，识别不通过则直接提示用户识别失败，节省了识别的时间，提高了指纹传感器的工作效率；或者，
还可以直接从该目标指纹图像数据中提取该目标手指的待检特征点，直接将待检特征点与预设特征点进行匹配，匹配成功确认该目标手指的指纹识别成功。
在此，需要说明的是，上述仅为举例说明的一些指纹的识别方式，其他本领域技术人员能想到的指纹识别方式均包括在本发明实施例的保护范围内。
本发明实施例通过采集目标手指的多份指纹图像，以获取多份目标手指的初始指纹图像数据；然后计算多份初始指纹图像数据中对应像素点的像素值的平均值，并将该平均值映射到对应像素点以构成目标指纹图像数据。该方法利用均值降噪的方式提高了指纹图像的识别率，解决了由于指纹传感器在采集目标手指指纹图像数据时出现的噪点造成的指纹图像识别率低的问题，提升了用户的使用体验。
实施例二
图2为本发明实施例二提供的一种指纹识别的方法的流程图，本发明实施例二提供的指纹识别的方法以上述实施例的技术方案为基础。
参见图2，该方法包括如下操作：
S201、连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据。
S202、计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据。
S203、提取所述目标指纹图像数据中目标手指指纹的待检特征点。
指纹是由人体的表皮凸起或凹陷所形成的纹路。由于人的指纹具有唯一性，即如，每个人的指纹具有其独特的区别与其他个体的特征，例用肉眼观察到的指纹的特点有指纹包括斗形纹，这种指纹由多个同心圆或螺纹线组成；包括弓线纹，这种指纹像弓一样排布。这些肉眼观察到的指纹其实并不是连续的、平滑笔直的，而是经常会出现中断、分叉或转折的，而这些中断点、分叉点或转折点就是指纹的特征点，另外，指纹的特征点还可以包括一些分歧点、孤立点或环点等。
在此，需要说明的是，指纹传感器对指纹的识别主要是对指纹的特征点的识别，一般情况下，指纹传感器并不会直接将采集到的手指的指纹图像进行保存，而是提取该手指的指纹图像的特征点，并将该特征点进行保存以继续使用。
因此，提取指纹的特征点对于指纹识别而言，是一项非常重要和关键的操作。在本实施例中，利用算法对用户的目标手指的目标指纹图像数据中的指纹特征点进行提取，并将提取到的特征点称为待检特征点。
另外，每次从目标指纹图像数据中提取的目标手指指纹的待检特征点的数量和/或参数值有可能存在不同，例如，由于每次采集得到的目标指纹图像数据有所差异，以使得从该目标指纹图像数据中提取的待检特征点也不完全相同。其中，待检特征点的参数可以包括特征点的坐标值和类型值，其中坐标值表示该特征点的位置，特征点的类型包括中心点、分叉点、终止点和三角点，例如，特征点A的坐标为（x,y），类型为分叉点。
当然，指纹的特征点还可以包括其他的参数值，例如，可以包括表示特征点朝向一定方向的方向参数，或表示特征点方向改变的快慢的曲率参数等。表示特征点的参数越多，对于指纹的描述越精细，指纹传感器识别的准确性也就越高。
S204、将所述待检特征点与预设指纹特征点进行匹配。
预设指纹特征点是在指纹传感器第一次识别到某一用户的特定手指的情况下，提取该用户处理后的指纹图像数据中特定手指的指纹的特征点，并将其进行保存，以形成的预设指纹特征点模板。该预设指纹特征点模板的作用是在后续操作中用于识别相应用户的特定手指的指纹。
在此，应该注意的是，该预设指纹特征点模板中的特征点只是以第一次识别得到的处理指纹图像数据为基础提取得到的手指的特征点，并不能包括该手指的全部特征点。
在提取得到目标手指的指纹特征点之后，将该待检特征点与预设指纹特征点进行匹配。该匹配可以是根据待检特征点的参数值进行匹配。优选的，根据待检特征点的坐标值和类型值进行匹配。即可以在预设指纹特征点中确认是否存在与待检特征点坐标值相同的特征点，若存在，则再确定预设指纹特征点的类型值是否与待检特征点的类型值相同，若相同，则确认该待检特征点匹配通过。
S205、在匹配通过时确认所述目标手指为安全手指。
在待检特征点与预设指纹特征点匹配通过时，确认该目标手指为安全手指。以指纹传感器内置于智能手机为例，此时，处于待机状态的智能手机可以直接进行工作状态，或者还可以按照采集的目标手指的不同，进入与该目标手指相对应的智能手机的应用程序界面等。
若待检特征点与预设指纹特征点未通过匹配，则指纹传感器提示用户继续进行指纹采集，若指纹采集规定次数次以上仍然不能通过，则提示用户以输入密码的方式进行认证；或者，提示用户指纹采集已经超过规定次数，请在规定时间后重新进行采集等。
对于步骤S204和步骤S205，更进一步可以通过如下方案具体实现：
基于预设指纹特征点对所述待检特征点进行逐一匹配；
在匹配通过时确定所述待检特征点为安全特征点；
计算所述安全特征点的数量；
当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
将待检特征点与预设指纹特征点模板进行逐一匹配，之后将待检特征点分为三种，一种是在上述匹配过程中待检特征点的参数与预设指纹特征点模板中的某一特征点的参数匹配成功，此时，将该待检特征点确认为安全特征点；第二种是在上述匹配过程中待检特征点的参数与预设指纹特征点模板中的特征点的某一参数未匹配成功，此时，该待检特征点对目标手指的匹配无贡献，直接丢弃该待检特征点；第三种是在上述匹配过程中预设指纹特征点模板中不存在与待检特征点参数值对应的特征点，即该待检特征点的参数均未匹配成功，此时，将该待检特征点参数均未匹配成功的待检特征点确定为第一特征点，用于在后续操作中进一步更新预设特征点模板。
优选的，以待检特征点参数为坐标值和类型值为例进行说明。某一待检特征点A的坐标值为（x，y），类型值为分叉点。在与预设指纹特征点匹配的过程中，若坐标值和类型值均匹配成功，则确认该待检特征点为安全特征点；若只有坐标值或类型值匹配成功，则丢弃该待检特征点；若该待检特征点的参数值和类型值均未匹配成功，则确认该待检特征点为第一特征点，用以在后续操作中对预设指纹特征点模板进行更新。
通过指纹识别装置中内置的程序计算上述确定为安全特征点的数量，并将其与预设安全数量进行比对，当安全特征点的数量大于或等于预设安全数量时，则指纹识别装置可以确认该目标手指为安全手指。以指纹识别装置内置于智能手机中为例，此时，智能手机由待机状态进入到工作状态，或者进一步的，该智能手机可以直接进入与该手指匹配的应用程序的主界面中，例如，当指纹识别装置识别到该安全手指为用户的食指时，进入与之相对应的短信编辑主界面，当指纹识别装置识别到该安全手指为用户的中指时，进入与之相对应的网页浏览主界面，当指纹识别装置识别到该安全手指为用户的拇指时，进入与之相对应的拍照主界面等。这样设置的好处是，在智能手机由待机状态进入工作状态后，省去了用户需要再次对常用应用程序进行选择的操作，一方面，节省了用户的操作时间，提高了用户的使用体验；另一方面，减少了对设备操作，延长了设备的待机时长和使用寿命。
当然，本领域技术人员应该知晓，用户的手指与智能手机的应用程序的对应关系可以根据用户的需求灵活设置，上述手指和应用程序的对应关系仅为距离说明，并非对本发明实施例的限定。
另外，预设安全数量的设定可以是人为预先设定或指纹传感器装置默认设定。通常情况下，当采集到的一个手指的特征点在50个左右时，指纹传感器便可对该手指进行精确的识别。因此，在设定该预设安全数量时，可以参考特征点数量50个进行设定；或者也可以根据指纹传感器的工作场景或用户需求进行灵活设定。
S206、提取所述目标指纹图像数据中的待检特征点。
S207、基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点。
在此，需要说明的是，由目标手指的处理指纹图像数据提取得到的待检特征点并非与预设特征点模板中的特征点完全相同，有可能该待检特征点的数量小于预设特征点的数量，或者也有可能待检特征点的数量大于或者等于预设特征点的数量。当待检特征点的数量大于或等于预设特征点的数量时，可以说明此时获取的目标手指的处理指纹图像数据中包含了关于该目标手指指纹更多的特征点，此时，将该多出的未匹配成功的待检特征点称为第一特征点。
S208、将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为待检特征点的参数均未匹配成功的特征点。
在本发明实施例中，在预设特征点的基础上，将上述第一特征点增加到预设特征点模板中，以使得预设特征点模板中的关于用户目标手指的特征点更加全面，使得后续操作中指纹传感器的识别更加准确。
当然，本领域技术人员应该知晓，上述方式仅为更新预设特征点模板的一种方式，进一步的，还可以以其他方式对预设特征点模板进行更新，例如，还可以将全部待检特征点代替预设特征点模板中的特征点，以此更新预设特征点模板。
在此，需要说明的是，步骤S206-S208为在上述实施例的技术方案的基础上，在步骤S205之后，进一步增加的技术方案。本领域技术人员应该知晓，步骤S201-S205的技术方案可以单独实施，也可以同步骤S206-S208同时实施。
本发明实施例通过采集目标手指的多份指纹图像，以获取多份目标手指的初始指纹图像数据；然后计算多份初始指纹图像数据中对应像素点的像素值的平均值，并将该平均值映射到对应像素点以构成目标指纹图像数据。然后利用该目标指纹图像数据中提取到的指纹的特征点对该目标手指的指纹进行匹配，匹配通过则确认该目标手指为安全手指。并且，在确认其为安全手指之后，还可以进一步根据提取到的指纹特征点对预设指纹特征点模板进行更新，该方法利用均值降噪的方式提高了指纹图像的识别率，解决了由于指纹传感器在采集目标手指指纹图像数据时出现的噪点造成的指纹图像识别率低的问题，提升了用户的使用体验。
实施例三
图3为本发明实施例三提供的一种指纹识别的装置的结构示意图。该指纹识别的装置的实施例基于上述各指纹识别的方法的实施例实现，在指纹识别的装置实施例中未尽的描述，可以参考上述各个指纹识别的方法的实施例。
参见图3，该装置包括如下模块：采集模块31、计算模块32和识别模块33；
其中，采集模块31，用于连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；
计算模块32，用于计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；
识别模块33，用于根据所述目标指纹图像数据进行指纹识别。
本发明实施例通过采集模块31对目标手指进行多次指纹图像采集，以获取多份目标手指的初始指纹图像数据，然后通过计算模块32计算该多份初始指纹图像数据对应像素点的像素值的平均值，并将该平均值作为该像素点的像素值构成目标指纹图像数据，进而通过识别模块33对目标手指的指纹进行识别。本发明实施例利用均值降噪的方法提高了指纹图像的识别率，解决了由于指纹传感器在采集目标手指指纹图像数据时出现的噪点造成的指纹图像识别率低的问题，提升了用户的使用体验。
进一步的，识别模块33包括：
提取单元331，用于提取所述目标指纹图像数据中目标手指指纹的待检特征点；
匹配单元332，用于将所述待检特征点与预设指纹特征点进行匹配；
确认单元333，用于在匹配通过时确认所述目标手指为安全手指。
优选的，匹配单元332包括：
逐一匹配子单元3321，用于基于预设指纹特征点对所述待检特征点进行逐一匹配；
安全特征点确认子单元3322，用于在匹配通过时确定所述待检特征点为安全特征点；
数量计算子单元3323，用于计算所述安全特征点的数量；
所述确认单元333具体用于：
当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
在上述方案基础上，进一步的，该装置还包括：
提取模块34，用于在匹配通过时确认所述目标手指为安全手指之后，提取所述目标指纹图像数据中的待检特征点；
第一特征点确认模块35，用于基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点；
更新模块36，用于将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为所述待检特征点的参数均未匹配成功的特征点。
优选的，所述待检特征点的参数包括坐标值和类型值，其中，所述待检特征点的类型包括中心点、分叉点、终止点和三角点。
实施例四
相应的，本发明实施例还提供一种终端，如图4所示，该终端可以包括射频（RF，Radio
Frequency）电路401、包括有一个或一个以上计算机可读存储介质的存储器402、输入单元403、显示单元404、传感器405、音频电路406、无线保真（WiFi，Wireless
Fidelity)模块407、包括有一个或者一个以上处理核心的处理器408、以及电源409等部件。本领域技术人员可以理解，图4中示出的终端结构并不构成对终端的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。其中：
RF电路401可用于收发信息或通话过程中，信号的接收和发送，特别地，将基站的下行信息接收后，交由一个或者一个以上处理器408处理；另外，将涉及上行的数据发送给基站。通常，RF电路401包括但不限于天线、至少一个放大器、调谐器、一个或多个振荡器、用户身份模块（SIM，
Subscriber Identity Module）卡、收发信机、耦合器、低噪声放大器（LNA，Low Noise
Amplifier）、双工器等。此外，RF电路401还可以通过无线通信与网络和其他设备通信。所述无线通信可以使用任一通信标准或协议，包括但不限于全球移动通讯系统
（GSM，Global System of Mobile communication）、通用分组无线服务（GPRS ，General Packet Radio
Service）、码分多址（CDMA，Code Division Multiple Access）、宽带码分多址（WCDMA，Wideband Code
Division Multiple Access）、长期演进（LTE，Long Term Evolution)、电子邮件、短消息服务（SMS，Short
Messaging Service)等。
存储器402可用于存储软件程序以及模块，处理器408通过运行存储在存储器402的软件程序以及模块，从而执行各种功能应用以及数据处理。存储器402可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序（比如声音播放功能、图像播放功能等）等；存储数据区可存储根据终端的使用所创建的数据（比如音频数据、电话本等）等。此外，存储器402可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。相应地，存储器402还可以包括存储器控制器，以提供处理器408和输入单元403对存储器402的访问。
输入单元403可用于接收输入的数字或字符信息，以及产生与用户设置以及功能控制有关的键盘、鼠标、操作杆、光学或者轨迹球信号输入。具体地，在一个具体的实施例中，输入单元403可包括触敏表面以及其他输入设备。触敏表面，也称为触摸显示屏或者触控板，可收集用户在其上或附近的触摸操作（比如用户使用手指、触笔等任何适合的物体或附件在触敏表面上或在触敏表面附近的操作），并根据预先设定的程式驱动相应的连接装置。可选的，触敏表面可包括触摸检测装置和触摸控制器两个部分。其中，触摸检测装置检测用户的触摸方位，并检测触摸操作带来的信号，将信号传送给触摸控制器；触摸控制器从触摸检测装置上接收触摸信息，并将它转换成触点坐标，再送给处理器408，并能接收处理器408发来的命令并加以执行。此外，可以采用电阻式、电容式、红外线以及表面声波等多种类型实现触敏表面。除了触敏表面，输入单元403还可以包括其他输入设备。具体地，其他输入设备可以包括但不限于物理键盘、功能键（比如音量控制按键、开关按键等）、轨迹球、鼠标、操作杆等中的一种或多种。
显示单元404可用于显示由用户输入的信息或提供给用户的信息以及终端的各种图形用户接口，这些图形用户接口可以由图形、文本、图标、视频和其任意组合来构成。显示单元404可包括显示面板，可选的，可以采用液晶显示器（LCD，Liquid
Crystal Display）、有机发光二极管（OLED，Organic Light-Emitting
Diode）等形式来配置显示面板。进一步的，触敏表面可覆盖显示面板，当触敏表面检测到在其上或附近的触摸操作后，传送给处理器408以确定触摸事件的类型，随后处理器408根据触摸事件的类型在显示面板上提供相应的视觉输出。虽然在图4中，触敏表面与显示面板是作为两个独立的部件来实现输入和输入功能，但是在某些实施例中，可以将触敏表面与显示面板集成而实现输入和输出功能。
终端还可包括至少一种传感器405，比如光传感器、运动传感器以及其他传感器。具体地，光传感器可包括环境光传感器及接近传感器，其中，环境光传感器可根据环境光线的明暗来调节显示面板的亮度，接近传感器可在终端移动到耳边时，关闭显示面板和/或背光。作为运动传感器的一种，重力加速度传感器可检测各个方向上（一般为三轴）加速度的大小，静止时可检测出重力的大小及方向，可用于识别手机姿态的应用（比如横竖屏切换、相关游戏、磁力计姿态校准）、振动识别相关功能（比如计步器、敲击）等;
至于终端还可配置的陀螺仪、气压计、湿度计、温度计、红外线传感器等其他传感器，在此不再赘述。
音频电路406、扬声器，传声器可提供用户与终端之间的音频接口。音频电路406可将接收到的音频数据转换后的电信号，传输到扬声器，由扬声器转换为声音信号输出；另一方面，传声器将收集的声音信号转换为电信号，由音频电路406接收后转换为音频数据，再将音频数据输出处理器408处理后，经RF电路401以发送给比如另一终端，或者将音频数据输出至存储器402以便进一步处理。音频电路406还可能包括耳塞插孔，以提供外设耳机与终端的通信。
WiFi属于短距离无线传输技术，终端通过WiFi模块407可以帮助用户收发电子邮件、浏览网页和访问流式媒体等，它为用户提供了无线的宽带互联网访问。虽然图4示出了WiFi模块407，但是可以理解的是，其并不属于终端的必须构成，完全可以根据需要在不改变发明的本质的范围内而省略。
处理器408是终端的控制中心，利用各种接口和线路连接整个手机的各个部分，通过运行或执行存储在存储器402内的软件程序和/或模块，以及调用存储在存储器402内的数据，执行终端的各种功能和处理数据，从而对手机进行整体监控。可选的，处理器408可包括一个或多个处理核心；优选的，处理器408可集成应用处理器和调制解调处理器，其中，应用处理器主要处理操作系统、用户界面和应用程序等，调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器408中。
终端还包括给各个部件供电的电源409（比如电池），优选的，电源可以通过电源管理系统与处理器408逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。电源409还可以包括一个或一个以上的直流或交流电源、再充电系统、电源故障检测电路、电源转换器或者逆变器、电源状态指示器等任意组件。
尽管未示出，终端还可以包括摄像头、蓝牙模块等，在此不再赘述。具体在本实施例中，终端中的处理器408会按照如下的指令，将一个或一个以上的应用程序的进程对应的可执行文件加载到存储器402中，并由处理器408来运行存储在存储器402中的应用程序，从而实现各种功能：
连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；
计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；
根据所述目标指纹图像数据进行指纹识别。
以上各个操作的具体实施可参见前面的实施例，在此不再赘述。
该终端可以实现本发明实施例所提供的任一种指纹识别的装置所能实现的有效效果，详见前面的实施例，在此不再赘述。
注意，上述仅为本发明的较佳实施例及所运用技术原理。本领域技术人员会理解，本发明不限于这里所述的特定实施例，对本领域技术人员来说能够进行各种明显的变化、重新调整和替代而不会脱离本发明的保护范围。因此，虽然通过以上实施例对本发明进行了较为详细的说明，但是本发明不仅仅限于以上实施例，在不脱离本发明构思的情况下，还可以包括更多其他等效实施例，而本发明的范围由所附的权利要求范围决定。
Claims (20)
- 一种指纹识别的方法，其包括：连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；根据所述目标指纹图像数据进行指纹识别。
- 根据权利要求1所述的指纹识别的方法，其中所述计算每份所述初始指纹图像数据对应像素点的像素值的平均值，包括：获取每份初始指纹图像数据中像素点的坐标值；根据所述坐标值确定所有初始指纹图像数据中位于相同位置的像素点，并计算位于相同位置的像素点的像素值之和；根据所述像素值之和计算对应像素点的像素值的平均值。
- 根据权利要求1所述的指纹识别的方法，其中所述根据所述目标图像数据进行指纹识别，包括：提取所述目标指纹图像数据中目标手指指纹的待检特征点；将所述待检特征点与预设指纹特征点进行匹配；在匹配通过时确认所述目标手指为安全手指。
- 根据权利要求3所述的指纹识别的方法，其中所述将所述待检特征点与预设指纹特征点进行匹配，包括：基于预设指纹特征点对所述待检特征点进行逐一匹配；在匹配通过时确定所述待检特征点为安全特征点；计算所述安全特征点的数量；所述在匹配通过时确认所述目标手指为安全手指，具体为：当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
- 根据权利要求3所述的指纹识别的方法，其中所述在匹配通过时确认所述目标手指为安全手指之后，还包括：提取所述目标指纹图像数据中的待检特征点；基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点；将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为待检特征点的参数均未匹配成功的特征点。
- 根据权利要求3所述的指纹识别的方法，其中在将所述待检特征点与预设指纹特征点进行匹配之前，还包括：提取所述目标指纹图像数据中目标手指指纹的待检纹路；将所述待检纹路与预设纹路进行匹配；若匹配成功，则执行将所述待检特征点与预设指纹特征点进行匹配的操作。
- 根据权利要求3-6任一所述的指纹识别的方法，其中所述待检特征点的参数包括坐标值和类型值，其中，所述待检特征点的类型包括中心点、分叉点、终止点和三角点。
- 一种指纹识别的装置，其包括：采集模块，用于连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；计算模块，用于计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；识别模块，用于根据所述目标指纹图像数据进行指纹识别。
- 根据权利要求8所述的指纹识别的装置，其中所述计算模块具体用于：获取每份初始指纹图像数据中像素点的坐标值；根据所述坐标值确定所有初始指纹图像数据中位于相同位置的像素点，并计算位于相同位置的像素点的像素值之和；根据所述像素值之和计算对应像素点的像素值的平均值。
- 根据权利要求8所述的指纹识别的装置，其中所述识别模块包括：提取单元，用于提取所述目标指纹图像数据中目标手指指纹的待检特征点；匹配单元，用于将所述待检特征点与预设指纹特征点进行匹配；确认单元，用于在匹配通过时确认所述目标手指为安全手指。
- 根据权利要求10所述的指纹识别的装置，其中所述匹配单元包括：逐一匹配子单元，用于基于预设指纹特征点对所述待检特征点进行逐一匹配；安全特征点确认子单元，用于在匹配通过时确定所述待检特征点为安全特征点；数量计算子单元，用于计算所述安全特征点的数量；所述确认单元具体用于：当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
- 根据权利要求10所述的指纹识别的装置，其还包括：提取模块，用于在匹配通过时确认所述目标手指为安全手指之后，提取所述目标指纹图像数据中的待检特征点；第一特征点确认模块，用于基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点；更新模块，用于将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为待检特征点的参数均未匹配成功的特征点。
- 根据权利要求10所述的指纹识别的装置，其中所述识别模块还用于：在将所述待检特征点与预设指纹特征点进行匹配之前，提取所述目标指纹图像数据中目标手指指纹的待检纹路；将所述待检纹路与预设纹路进行匹配；若匹配成功，则执行将所述待检特征点与预设指纹特征点进行匹配的操作。
- 根据权利要求10-13任一所述的指纹识别的装置，其中所述待检特征点的参数包括坐标值和类型值，其中，所述待检特征点的类型包括中心点、分叉点、终止点和三角点。
- 一种终端，其包括存储器和处理器，所述存储器存储有处理器可执行的指令，所述处理器用于执行所述存储器中的指令，所述指令用于执行如下操作：连续对目标手指进行至少两次指纹图像采集，获取至少两份所述目标手指的初始指纹图像数据；计算每份所述初始指纹图像数据对应像素点的像素值的平均值，将所述平均值映射到对应的像素点构成目标指纹图像数据；根据所述目标指纹图像数据进行指纹识别。
- 根据权利要求15所述的终端，其中所述指令还用于执行如下操作：：获取每份初始指纹图像数据中像素点的坐标值；根据所述坐标值确定所有初始指纹图像数据中位于相同位置的像素点，并计算位于相同位置的像素点的像素值之和；根据所述像素值之和计算对应像素点的像素值的平均值。
- 根据权利要求15所述的终端，其中所述指令还用于执行如下操作：提取所述目标指纹图像数据中目标手指指纹的待检特征点；将所述待检特征点与预设指纹特征点进行匹配；在匹配通过时确认所述目标手指为安全手指。
- 根据权利要求17所述的终端，其中所述指令还用于执行如下操作：基于预设指纹特征点对所述待检特征点进行逐一匹配；在匹配通过时确定所述待检特征点为安全特征点；计算所述安全特征点的数量；所述在匹配通过时确认所述目标手指为安全手指，具体为：当所述安全特征点的数量大于或等于预设安全数量时，确认所述目标手指为安全手指。
- 根据权利要求17所述的终端，其中所述指令还用于执行如下操作：提取所述目标指纹图像数据中的待检特征点；基于预设指纹特征点对所述待检特征点进行匹配，以确定所述待检特征点中的第一特征点；将所述第一特征点保存在所述预设指纹特征点中，以更新所述预设指纹特征点；其中，所述第一特征点为待检特征点的参数均未匹配成功的特征点。
- 根据权利要求17-19任一所述的终端，其中所述待检特征点的参数包括坐标值和类型值，其中，所述待检特征点的类型包括中心点、分叉点、终止点和三角点。
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| CN110555348A (zh) * | 2018-06-01 | 2019-12-10 | 中兴通讯股份有限公司 | 一种指纹识别方法、设备及计算机可读存储介质 |
| CN116386091A (zh) * | 2022-11-18 | 2023-07-04 | 荣耀终端有限公司 | 指纹识别方法和装置 |
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| CN108021912B (zh) * | 2015-10-19 | 2021-06-29 | Oppo广东移动通信有限公司 | 一种指纹识别的方法和装置 |
| CN107563262A (zh) * | 2016-07-01 | 2018-01-09 | 深圳芯启航科技有限公司 | 基于指纹识别的方向导航方法、装置及指纹图像传感器 |
| CN106682473A (zh) * | 2016-12-20 | 2017-05-17 | 深圳芯启航科技有限公司 | 一种用户身份信息的识别方法及其装置 |
| CN106980842B (zh) | 2017-04-01 | 2020-02-18 | 京东方科技集团股份有限公司 | 指纹识别模块和显示基板 |
| SE1750530A1 (en) * | 2017-05-02 | 2018-11-03 | Fingerprint Cards Ab | Extracting fingerprint feature data from a fingerprint image |
| CN107590434A (zh) * | 2017-08-09 | 2018-01-16 | 广东欧珀移动通信有限公司 | 识别模型更新方法、装置和终端设备 |
| CN107590475A (zh) * | 2017-09-22 | 2018-01-16 | 北京小米移动软件有限公司 | 指纹识别的方法和装置 |
| CN111222367B (zh) * | 2018-11-26 | 2023-11-10 | 上海耕岩智能科技有限公司 | 一种指纹识别方法及装置、存储介质、终端 |
| CN109902569B (zh) * | 2019-01-23 | 2021-09-17 | 上海思立微电子科技有限公司 | 指纹图像的转换方法、装置和指纹识别方法 |
| WO2020223881A1 (zh) * | 2019-05-06 | 2020-11-12 | 深圳市汇顶科技股份有限公司 | 指纹检测的方法、装置和电子设备 |
| TWI766474B (zh) * | 2020-06-15 | 2022-06-01 | 神盾股份有限公司 | 指紋感測裝置以及指紋感測方法 |
| CN114373196B (zh) * | 2021-12-31 | 2023-09-19 | 天津极豪科技有限公司 | 有效采集区域确定方法、程序产品、存储介质及电子设备 |
| CN117173818B (zh) * | 2023-10-16 | 2024-06-18 | 康运达控股(山西)有限公司 | 一种基于指纹识别的智能锁使用方法 |
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| CN116386091B (zh) * | 2022-11-18 | 2024-04-02 | 荣耀终端有限公司 | 指纹识别方法和装置 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108021912A (zh) | 2018-05-11 |
| CN105224930A (zh) | 2016-01-06 |
| CN105224930B (zh) | 2018-03-06 |
| CN108021912B (zh) | 2021-06-29 |
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