A method, device and storage medium for processing fingerprint images

Abstract

The present disclosure relates to a method, device and storage medium for processing fingerprint images, the method comprising: calculating feature information of a fingerprint grayscale image; determining a binarization threshold according to the feature information; binarizing the fingerprint image according to the binarization threshold; and performing fingerprint recognition based on the binarized image. In the present disclosure, the binarization threshold is determined according to the feature information of the fingerprint grayscale image, and different binarization thresholds are used for different fingerprint grayscale images. The determined binarization threshold can better match the grayscale distribution of the fingerprint grayscale image, thereby improving the fingerprint recognition accuracy. In particular, in the case where the fingerprint image contains a lot of noise, such as when the user's finger is wet or greasy, or when the finger is relatively dry, the fingerprint recognition accuracy can be significantly improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of mobile terminal data processing, and in particular to a method, device and storage medium for processing fingerprint images. Background Art

[0002] In the process of fingerprint recognition through a touch screen, the method of using a fixed threshold to binarize the fingerprint image seriously affects the fingerprint unlocking success rate when many fingerprint images are not clear enough. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a method, device and storage medium for processing fingerprint images.

[0004] According to a first aspect of an embodiment of this document, a method for processing a fingerprint image is provided, which is applied to a terminal with a touch screen, and includes:

[0005] Calculate the feature information of the fingerprint grayscale image;

[0006] Determine a binarization threshold according to the feature information;

[0007] Performing binarization processing on the fingerprint image according to the binarization threshold;

[0008] Fingerprint recognition is performed based on the binary image.

[0009] In another embodiment, the calculating feature information of the fingerprint grayscale image includes: calculating a representative grayscale value of the fingerprint grayscale image;

[0010] The determining of the binarization threshold according to the feature information includes: determining the binarization threshold according to the representative grayscale value of the fingerprint grayscale image.

[0011] In another embodiment, the calculating the representative grayscale value of the fingerprint grayscale image includes one of the following:

[0012] Calculate the average value of the grayscale values ​​of all pixels in the fingerprint grayscale image, and use the average value as the representative grayscale value;

[0013] Sorting the grayscale values ​​of all pixels in the fingerprint grayscale image from large to small, calculating the average value of a set number of grayscale values ​​located in the middle area of ​​the sorted sequence, and taking the calculated average value as the representative grayscale value;

[0014] Determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and determine the representative grayscale value according to the grayscale value in the central area.

[0015] In another embodiment, determining the binarization threshold according to the representative grayscale value of the fingerprint grayscale image includes one of the following:

[0016] Using the representative grayscale value as a binarization threshold;

[0017] A one-to-one mapping relationship between grayscale value intervals and binarization thresholds is set; different grayscale value intervals correspond to different binarization thresholds; the grayscale value interval to which the representative grayscale value belongs is determined, and the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs is determined.

[0018] In another embodiment, the calculating feature information of the fingerprint grayscale image includes: calculating a representative grayscale value and a representative gradient value of the fingerprint grayscale image;

[0019] Determining the binarization threshold according to the feature information includes: determining an increment value according to the representative gradient value, and taking the sum of the representative gray value and the increment value as the binarization threshold.

[0020] In another embodiment, the calculating the representative gradient value of the fingerprint grayscale image includes one of the following:

[0021] Calculate the difference between the maximum grayscale value and the minimum grayscale value in the fingerprint grayscale image, and use the difference as the representative gradient value;

[0022] The grayscale values ​​of all pixels in the fingerprint grayscale image are sorted from large to small, and the difference between the average value of the first N grayscale values ​​and the average value of the last M grayscale values ​​in the sorted sequence is calculated, and the calculated difference is used as the representative gradient value; wherein N and M are integers greater than 1.

[0023] In another embodiment, the method further includes: setting a one-to-one mapping relationship between the grayscale weight value interval and the binarization threshold; different grayscale weight value intervals correspond to different binarization thresholds;

[0024] The calculating feature information of the fingerprint grayscale image includes: calculating the grayscale weight value of the fingerprint grayscale image;

[0025] Determining the binarization threshold according to the feature information includes: determining the grayscale weight value interval to which the representative grayscale weight value belongs, and determining the binarization threshold corresponding to the grayscale weight value interval to which the representative grayscale weight value belongs.

[0026] In another embodiment, the calculation of the grayscale weight value of the fingerprint grayscale image includes: determining the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determining the central area of ​​a set width corresponding to the peak of the statistical distribution result, and calculating the ratio of the average grayscale value in the central area to the set maximum grayscale value as the representative grayscale weight value.

[0027] According to a second aspect of the embodiments of this document, there is provided a device for processing a fingerprint image, which is applied to a terminal with a touch screen and is characterized by comprising:

[0028] A calculation module, used for calculating feature information of a fingerprint grayscale image;

[0029] A determination module, used to determine a binarization threshold according to the feature information;

[0030] A processing module, performing binarization processing on the fingerprint image according to the binarization threshold;

[0031] The recognition module performs fingerprint recognition based on the binary processed image.

[0032] In another embodiment, the calculation module includes a first operation module for calculating a representative grayscale value of the fingerprint grayscale image;

[0033] The determination module is further configured to determine a binarization threshold value according to the feature information using the following method: determining the binarization threshold value according to the representative grayscale value of the fingerprint grayscale image.

[0034] In another embodiment, the first computing module is used to calculate the representative grayscale value of the fingerprint grayscale image using one of the following methods:

[0035] Calculate the average value of the grayscale values ​​of all pixels in the fingerprint grayscale image, and use the average value as the representative grayscale value;

[0036] Sorting the grayscale values ​​of all pixels in the fingerprint grayscale image from large to small, calculating the average value of a set number of grayscale values ​​located in the middle area of ​​the sorted sequence, and taking the calculated average value as the representative grayscale value;

[0037] Determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and determine the representative grayscale value according to the grayscale value in the central area.

[0038] In another embodiment, the determination module is further configured to determine a binarization threshold according to the representative grayscale value of the fingerprint grayscale image using one of the following methods:

[0039] Using the representative grayscale value as a binarization threshold;

[0040] A one-to-one mapping relationship between grayscale value intervals and binarization thresholds is set; different grayscale value intervals correspond to different binarization thresholds; the grayscale value interval to which the representative grayscale value belongs is determined, and the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs is determined.

[0041] In another embodiment, the calculation module further includes a second operation module for calculating a representative grayscale value and a representative gradient value of the fingerprint grayscale image;

[0042] The determination module is further used to determine a binarization threshold according to the feature information using the following method: determining an increment value according to the representative gradient value, and taking the sum of the representative gray value and the increment value as the binarization threshold.

[0043] In another embodiment, the second operation module is further configured to calculate the representative gradient value of the fingerprint grayscale image using one of the following methods:

[0044] Calculate the difference between the maximum grayscale value and the minimum grayscale value in the fingerprint grayscale image, and use the difference as the representative gradient value;

[0045] The grayscale values ​​of all pixels in the fingerprint grayscale image are sorted from large to small, and the difference between the average value of the first N grayscale values ​​and the average value of the last M grayscale values ​​in the sorted sequence is calculated, and the calculated difference is used as the representative gradient value; wherein N and M are integers greater than 1.

[0046] In another embodiment, the device further comprises: a setting module, configured to set a one-to-one mapping relationship between a grayscale weight value interval and a binarization threshold; different grayscale weight value intervals correspond to different binarization thresholds;

[0047] The calculation module includes a third operation module, which is used to calculate the grayscale weight value of the fingerprint grayscale image;

[0048] The determination module is also used to determine the binarization threshold according to the feature information using the following method: determine the grayscale weight value interval to which the representative grayscale weight value belongs, and determine the binarization threshold corresponding to the grayscale weight value interval to which the representative grayscale weight value belongs.

[0049] In another embodiment, the third operation module is also used to calculate the grayscale weight value of the fingerprint grayscale image using the following method: determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​a set width corresponding to the peak value of the statistical distribution result, and calculate the ratio of the average grayscale value in the central area to the set maximum grayscale value as the representative grayscale weight value.

[0050] According to a third aspect of the embodiments of this document, there is provided a device for processing a fingerprint image, comprising:

[0051] processor;

[0052] a memory for storing processor-executable instructions;

[0053] Wherein, the processor is configured to:

[0054] Calculate the feature information of the fingerprint grayscale image;

[0055] Determine a binarization threshold according to the feature information;

[0056] Performing binarization processing on the fingerprint image according to the binarization threshold;

[0057] Fingerprint recognition is performed based on the binary image.

[0058] According to a fourth aspect of the embodiments of this document, a non-transitory computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of a terminal, a method for enabling the terminal to process a fingerprint image includes:

[0059] Calculate the feature information of the fingerprint grayscale image;

[0060] Determine a binarization threshold according to the feature information;

[0061] Performing binarization processing on the fingerprint image according to the binarization threshold;

[0062] Fingerprint recognition is performed based on the binary image.

[0063] The technical solution provided by the embodiments of the present disclosure may include the following beneficial effects: determining the binarization threshold according to the characteristic information of the fingerprint grayscale image, using different binarization thresholds for different fingerprint grayscale images, and the determined binarization threshold can better match the grayscale distribution of the fingerprint grayscale image, thereby improving the fingerprint recognition accuracy. In particular, when the fingerprint image contains a lot of noise, such as when the user's finger is wet or greasy, or the finger is relatively dry, the fingerprint recognition accuracy can be significantly improved.

[0064] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0066] Figure 1is a flow chart of a method for binarizing a fingerprint image according to an exemplary embodiment;

[0067] Figure 2 is a structural diagram of a device for performing binarization processing on a fingerprint image according to an exemplary embodiment;

[0068] Figure 3 The figure is a structural diagram of a device for performing binarization processing on a fingerprint image according to an exemplary embodiment. DETAILED DESCRIPTION

[0069] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0070] A method for processing a fingerprint image is provided in an embodiment of the present disclosure. Figure 1 is a flow chart of a method for processing a fingerprint image according to an exemplary embodiment, and the method is applied to a terminal with a touch screen, such as Figure 1 As shown, this method includes:

[0071] Step S11, calculating feature information of the fingerprint grayscale image.

[0072] Step S12: determining a binarization threshold according to the feature information.

[0073] Step S13, binarizing the fingerprint image according to the binarization threshold.

[0074] Step S14, fingerprint recognition is performed based on the binarized image.

[0075] In this embodiment, the binarization threshold is determined according to the characteristic information of the fingerprint grayscale image, and different binarization thresholds are used for different fingerprint grayscale images. The determined binarization threshold can better match the grayscale distribution of the fingerprint grayscale image, thereby improving the fingerprint recognition accuracy. In particular, when the fingerprint image contains a lot of noise, such as when the user's finger is wet or greasy, or the finger is relatively dry, the fingerprint recognition accuracy can be significantly improved.

[0076] The present disclosure also provides a method for processing fingerprint images. The method includes Figure 1 The method shown, and Figure 1In step S11, characteristic information of the fingerprint grayscale image is calculated, including: step S111, calculating the representative grayscale value of the fingerprint grayscale image. In step S12, a binarization threshold is determined according to the characteristic information, including: step S121, determining the binarization threshold according to the representative grayscale value of the fingerprint grayscale image.

[0077] The grayscale value ranges from 0 to 255. When the grayscale value is 0, it corresponds to a pure black display effect, and when the grayscale value is 255, it corresponds to a pure black and white display effect.

[0078] The method for calculating the representative grayscale value of the fingerprint grayscale image in step S111 includes one of the following:

[0079] First, calculate the average grayscale value of all pixels in the fingerprint grayscale image and use the average value as the representative grayscale value.

[0080] For example, when a user needs to perform fingerprint recognition and presses the area on the touch screen for fingerprint recognition with a mobile phone, when the finger is dry, the average grayscale value of all pixels in the fingerprint grayscale image is calculated to be 220; when the finger is in a normal state, the average value is calculated to be 150; when the finger is wet, the average value is calculated to be 20.

[0081] Second, the grayscale values ​​of all pixels in the fingerprint grayscale image are sorted from large to small, and the average value of a set number of grayscale values ​​located in the middle area of ​​the sorted sequence is calculated, and the calculated average value is used as the representative grayscale value.

[0082] Third, determine the statistical distribution results of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and determine the representative grayscale value according to the grayscale value in the central area.

[0083] In one implementation, step S121, determining a binarization threshold according to a representative grayscale value of the fingerprint grayscale image, includes: using the representative grayscale value of the fingerprint grayscale image as the binarization threshold.

[0084] In one embodiment, step S121, determining a binarization threshold according to a representative grayscale value of a fingerprint grayscale image, includes: setting a one-to-one mapping relationship between a grayscale value interval and a binarization threshold; different grayscale value intervals correspond to different binarization thresholds; determining the grayscale value interval to which the representative grayscale value belongs, and determining the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs.

[0085] For example, two grayscale value intervals are set, namely the first interval, i.e., the interval [0-150) and the second interval, i.e., the interval [150-255]. The binarization threshold corresponding to the first interval is 140, and the binarization threshold corresponding to the second interval is 180. When the representative grayscale value belongs to the first interval, the determined binarization threshold is 140, and when the representative grayscale value belongs to the second interval, the determined binarization threshold is 180.

[0086] For example, three grayscale value intervals are set, namely the first interval [0-120], the second interval (120-160), and the third interval [160-255]. The binarization threshold corresponding to the first interval is 110, the binarization threshold corresponding to the second interval is 140, and the binarization threshold corresponding to the third interval is 180. When the representative grayscale value belongs to the first interval, the determined binarization threshold is 110, when the representative grayscale value belongs to the second interval, the determined binarization threshold is 140; when the representative grayscale value belongs to the third interval, the determined binarization threshold is 255.

[0087] The present disclosure also provides a method for processing fingerprint images. The method includes Figure 1 The method shown, and Figure 1 In the illustrated step S12, the characteristic information of the fingerprint grayscale image is calculated, including: calculating the representative grayscale value and the representative gradient value of the fingerprint grayscale image.

[0088] The method for calculating the representative grayscale value of the fingerprint grayscale image is the same as the method in the above embodiment.

[0089] The method for calculating the representative gradient value of the fingerprint grayscale image includes one of the following:

[0090] First, calculate the difference between the maximum grayscale value and the minimum grayscale value in the fingerprint grayscale image, and use the difference as the representative gradient value.

[0091] Second, sort the grayscale values ​​of all pixels in the fingerprint grayscale image from large to small, calculate the difference between the average value of the first N grayscale values ​​in the sorted sequence and the average value of the last M grayscale values, and use the calculated difference as the representative gradient value; wherein N and M are integers greater than 1.

[0092] Figure 1In the illustrated step S12, the binarization threshold is determined according to the feature information, including: determining the increment value according to the representative gradient value, and taking the sum of the representative grayscale value and the increment value as the binarization threshold. The representative gradient value is proportional to the increment value. The smaller the representative gradient value is, the more concentrated the grayscale value distribution in the fingerprint grayscale image is, and the more the representative grayscale value can represent most of the grayscale values ​​in the fingerprint grayscale image. The larger the representative gradient value is, the more dispersed the grayscale value distribution in the fingerprint grayscale image is, and it is necessary to increase the increment value on the basis of the representative grayscale value to better represent most of the grayscale values ​​in the fingerprint grayscale image.

[0093] In another embodiment, the method further includes: counting the number of pixels in the fingerprint grayscale image whose grayscale values ​​are greater than the representative grayscale value as a first number and the number of pixels whose grayscale values ​​are less than the representative grayscale value as a second number, when the first number is greater than the second number, the incremental value is a positive value, when the first number is equal to the second number, the incremental value is 0, and when the first number is less than the second number, the incremental value is a negative value.

[0094] The present disclosure provides a method for processing a fingerprint image, the method comprising: Figure 1 The method shown also includes: setting a one-to-one mapping relationship between the grayscale weight value interval and the binarization threshold; different grayscale weight value intervals correspond to different binarization thresholds.

[0095] Figure 1 The step S11 shown in FIG. 1 is to calculate the characteristic information of the fingerprint grayscale image, including: calculating the grayscale weight value of the fingerprint grayscale image.

[0096] The calculation of the grayscale weight value of the fingerprint grayscale image includes: determining a statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determining a central area of ​​a set width corresponding to a peak value of the statistical distribution result, and calculating a ratio of an average grayscale value in the central area to a set maximum grayscale value as a representative grayscale weight value.

[0097] Figure 1 The step S12 shown is to determine a binarization threshold value according to the feature information, including: determining the grayscale weight value interval to which the representative grayscale weight value belongs, and determining the binarization threshold value corresponding to the grayscale weight value interval to which the representative grayscale weight value belongs.

[0098] For example, two grayscale weight value intervals are set, namely the first interval, i.e., the [0-150 / 255) interval, and the second interval, i.e., the [150 / 255-1] interval. The binarization threshold corresponding to the first interval is 140, and the binarization threshold corresponding to the second interval is 180. When the grayscale weight value belongs to the first interval, the determined binarization threshold is 140, and when the grayscale weight value belongs to the second interval, the determined binarization threshold is 180.

[0099] An embodiment of the present disclosure provides a device for processing a fingerprint image. Figure 2 is a structural diagram of a device for processing a fingerprint image according to an exemplary embodiment, and the device is applied to a terminal with a touch screen, such as Figure 1 As shown, this method includes:

[0100] A calculation module 201 is used to calculate feature information of a fingerprint grayscale image;

[0101] A determination module 202, configured to determine a binarization threshold according to the feature information;

[0102] Processing module 203, binarization processing is performed on the fingerprint image according to the binarization threshold;

[0103] The identification module 204 performs fingerprint identification based on the binarized image.

[0104] The present disclosure also provides a device for processing fingerprint images, which includes: Figure 2 The modules shown in the figure, and the calculation module 201 includes a first operation module, which is used to calculate the representative grayscale value of the fingerprint grayscale image. The determination module 202 is also used to determine the binarization threshold according to the feature information using the following method: determine the binarization threshold according to the representative grayscale value of the fingerprint grayscale image.

[0105] In one embodiment, the first computing module is used to calculate the representative grayscale value of the fingerprint grayscale image using one of the following methods:

[0106] Calculate the average value of the grayscale values ​​of all pixels in the fingerprint grayscale image, and use the average value as the representative grayscale value;

[0107] Sorting the grayscale values ​​of all pixels in the fingerprint grayscale image from large to small, calculating the average value of a set number of grayscale values ​​located in the middle area of ​​the sorted sequence, and taking the calculated average value as the representative grayscale value;

[0108] Determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and determine the representative grayscale value according to the grayscale value in the central area.

[0109] In another embodiment, the determination module 202 is further configured to determine a binarization threshold according to the representative grayscale value of the fingerprint grayscale image using one of the following methods:

[0110] Using the representative grayscale value as a binarization threshold;

[0111] A one-to-one mapping relationship between grayscale value intervals and binarization thresholds is set; different grayscale value intervals correspond to different binarization thresholds; the grayscale value interval to which the representative grayscale value belongs is determined, and the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs is determined.

[0112] The present disclosure also provides a device for processing fingerprint images, which includes: Figure 2 The module shown in the figure, and the calculation module 201 includes a second operation module for calculating the representative grayscale value and the representative gradient value of the fingerprint grayscale image. The determination module 202 is also used to determine the binarization threshold according to the feature information using the following method: determining an increment value according to the representative gradient value, and taking the sum of the representative grayscale value and the increment value as the binarization threshold.

[0113] In another embodiment, the second computing module is further configured to calculate the representative gradient value of the fingerprint grayscale image using one of the following methods:

[0114] 1. Calculate the difference between the maximum grayscale value and the minimum grayscale value in the fingerprint grayscale image, and use the difference as the representative gradient value;

[0115] Second, sort the grayscale values ​​of all pixels in the fingerprint grayscale image from large to small, calculate the difference between the average value of the first N grayscale values ​​in the sorted sequence and the average value of the last M grayscale values, and use the calculated difference as the representative gradient value; wherein N and M are integers greater than 1.

[0116] The present disclosure also provides a device for processing fingerprint images, which includes: Figure 2 The module shown in the figure also includes: a setting module, which is used to set a one-to-one mapping relationship between the grayscale weight value interval and the binarization threshold; different grayscale weight value intervals correspond to different binarization thresholds;

[0117] The calculation module 201 includes a third operation module, which is used to calculate the grayscale weight value of the fingerprint grayscale image;

[0118] The determination module 202 is also used to determine a binarization threshold according to the feature information using the following method: determine the grayscale weight value interval to which the representative grayscale weight value belongs, and determine the binarization threshold corresponding to the grayscale weight value interval to which the representative grayscale weight value belongs.

[0119] The third operation module is also used to calculate the grayscale weight value of the fingerprint grayscale image using the following method: determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and calculate the ratio of the average grayscale value in the central area to the set maximum grayscale value as the representative grayscale weight value.

[0120] Figure 3 1 is a block diagram of a device 300 for processing a fingerprint image according to an exemplary embodiment. For example, the device 300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0121] Reference Figure 3 , the device 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0122] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 305 and the processing component 302.

[0123] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0124] The power supply component 306 provides power to the various components of the device 300. The power supply component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 300.

[0125] The multimedia component 305 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 305 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0126] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC), and when the device 300 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0127] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0128] The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300, the sensor assembly 314 can also detect the position change of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and the temperature change of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0129] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0130] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0131] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, and the instructions can be executed by the processor 320 of the device 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0132] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0133] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for processing a fingerprint image, applied to a terminal with a touch screen, characterized in that: include: Calculating feature information of the fingerprint grayscale image, wherein the feature information includes one of the following: a representative grayscale value, the representative grayscale value and a representative gradient value, and a grayscale weight value; Determine a binarization threshold according to the feature information; Performing binarization processing on the fingerprint image according to the binarization threshold; Perform fingerprint recognition based on the binary processed image; Wherein, when the characteristic information is the representative grayscale value, determining the binarization threshold according to the characteristic information includes: A one-to-one mapping relationship between grayscale value intervals and binarization thresholds is set; different grayscale value intervals correspond to different binarization thresholds; the grayscale value interval to which the representative grayscale value belongs is determined, and the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs is determined.

2. The method according to claim 1, characterized in that The calculating feature information of the fingerprint grayscale image includes: calculating the representative grayscale value of the fingerprint grayscale image.

3. The method according to claim 2, characterized in that The calculating the representative grayscale value of the fingerprint grayscale image includes one of the following: Calculate the average value of the grayscale values ​​of all pixels in the fingerprint grayscale image, and use the average value as the representative grayscale value; Sorting the grayscale values ​​of all pixels in the fingerprint grayscale image from large to small, calculating the average value of a set number of grayscale values ​​located in the middle area of ​​the sorted sequence, and taking the calculated average value as the representative grayscale value; Determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and determine the representative grayscale value according to the grayscale value in the central area.

4. The method according to claim 1, characterized in that The calculating feature information of the fingerprint grayscale image includes: calculating the representative grayscale value and the representative gradient value of the fingerprint grayscale image; Determining the binarization threshold according to the feature information includes: determining an increment value according to the representative gradient value, and taking the sum of the representative gray value and the increment value as the binarization threshold.

5. The method according to claim 4, characterized in that The calculating the representative gradient value of the fingerprint grayscale image includes one of the following: Calculate the difference between the maximum grayscale value and the minimum grayscale value in the fingerprint grayscale image, and use the difference as the representative gradient value; The grayscale values ​​of all pixels in the fingerprint grayscale image are sorted from large to small, and the difference between the average value of the first N grayscale values ​​and the average value of the last M grayscale values ​​in the sorted sequence is calculated, and the calculated difference is used as the representative gradient value; wherein N and M are integers greater than 1.

6. The method according to claim 1, characterized in that The method further comprises: setting a one-to-one mapping relationship between a grayscale weight value interval and a binarization threshold; different grayscale weight value intervals correspond to different binarization thresholds; The calculating feature information of the fingerprint grayscale image includes: calculating the grayscale weight value of the fingerprint grayscale image; Determining the binarization threshold according to the feature information includes: determining a grayscale weight value interval to which a representative grayscale weight value belongs, and determining a binarization threshold corresponding to the grayscale weight value interval to which the representative grayscale weight value belongs.

7. The method according to claim 6, characterized in that The calculation of the grayscale weight value of the fingerprint grayscale image includes: determining a statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determining a central area of ​​a set width corresponding to a peak value of the statistical distribution result, and calculating a ratio of an average grayscale value in the central area to a set maximum grayscale value as a representative grayscale weight value.

8. A device for processing fingerprint images, applied to a terminal with a touch screen, characterized in that: include: A calculation module, used to calculate feature information of a fingerprint grayscale image, wherein the feature information includes one of the following: a representative grayscale value, the representative grayscale value and a representative gradient value, and a grayscale weight value; A determination module, used to determine a binarization threshold according to the feature information; A processing module, performing binarization processing on the fingerprint image according to the binarization threshold; The recognition module performs fingerprint recognition based on the binary processed image; Wherein, when the characteristic information is the representative gray value, the determination module is used to determine the binarization threshold according to the characteristic information using the following method: A one-to-one mapping relationship between grayscale value intervals and binarization thresholds is set; different grayscale value intervals correspond to different binarization thresholds; the grayscale value interval to which the representative grayscale value belongs is determined, and the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs is determined.

9. The device according to claim 8, characterized in that The calculation module includes a first operation module, which is used to calculate the representative grayscale value of the fingerprint grayscale image.

10. The device according to claim 9, characterized in that The first computing module is configured to calculate the representative grayscale value of the fingerprint grayscale image using one of the following methods: Calculate the average value of the grayscale values ​​of all pixels in the fingerprint grayscale image, and use the average value as the representative grayscale value; Sorting the grayscale values ​​of all pixels in the fingerprint grayscale image from large to small, calculating the average value of a set number of grayscale values ​​located in the middle area of ​​the sorted sequence, and taking the calculated average value as the representative grayscale value; Determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and determine the representative grayscale value according to the grayscale value in the central area.

11. The device according to claim 8, characterized in that The calculation module further includes a second operation module, which is used to calculate the representative grayscale value and the representative gradient value of the fingerprint grayscale image; The determination module is further configured to determine a binarization threshold according to the feature information using the following method: determining an increment value according to the representative gradient value, and taking the sum of the representative grayscale value and the increment value as the binarization threshold.

12. The device according to claim 11, characterized in that The second operation module is further configured to calculate the representative gradient value of the fingerprint grayscale image using one of the following methods: Calculate the difference between the maximum grayscale value and the minimum grayscale value in the fingerprint grayscale image, and use the difference as the representative gradient value; The grayscale values ​​of all pixels in the fingerprint grayscale image are sorted from large to small, and the difference between the average value of the first N grayscale values ​​and the average value of the last M grayscale values ​​in the sorted sequence is calculated, and the calculated difference is used as the representative gradient value; wherein N and M are integers greater than 1.

13. The device according to claim 8, characterized in that The device further comprises: a setting module, which is used to set a one-to-one mapping relationship between a grayscale weight value interval and a binarization threshold; different grayscale weight value intervals correspond to different binarization thresholds; The calculation module includes a third operation module, which is used to calculate the grayscale weight value of the fingerprint grayscale image; The determination module is also used to determine the binarization threshold according to the feature information using the following method: determine the grayscale weight value interval to which the representative grayscale weight value belongs, and determine the binarization threshold corresponding to the grayscale weight value interval to which the representative grayscale weight value belongs.

14. The device according to claim 13, characterized in that The third operation module is also used to calculate the grayscale weight value of the fingerprint grayscale image using the following method: determine the statistical distribution result of the grayscale values ​​of all pixels in the fingerprint grayscale image, determine the central area of ​​the set width corresponding to the peak value of the statistical distribution result, and calculate the ratio of the average grayscale value in the central area to the set maximum grayscale value as the representative grayscale weight value.

15. A device for processing fingerprint images, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to: Calculating feature information of the fingerprint grayscale image, wherein the feature information includes one of the following: a representative grayscale value, the representative grayscale value and a representative gradient value, and a grayscale weight value; Determine a binarization threshold according to the feature information; Performing binarization processing on the fingerprint image according to the binarization threshold; Perform fingerprint recognition based on the binary processed image; Wherein, when the characteristic information is the representative grayscale value, determining the binarization threshold according to the characteristic information includes: A one-to-one mapping relationship between grayscale value intervals and binarization thresholds is set; different grayscale value intervals correspond to different binarization thresholds; the grayscale value interval to which the representative grayscale value belongs is determined, and the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs is determined.

16. A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of a terminal, enables the terminal to process a fingerprint image, comprising: Calculating feature information of the fingerprint grayscale image, wherein the feature information includes one of the following: a representative grayscale value, the representative grayscale value and a representative gradient value, and a grayscale weight value; Determine a binarization threshold according to the feature information; Performing binarization processing on the fingerprint image according to the binarization threshold; Perform fingerprint recognition based on the binary processed image; Wherein, when the characteristic information is the representative grayscale value, determining the binarization threshold according to the characteristic information includes: A one-to-one mapping relationship between grayscale value intervals and binarization thresholds is set; different grayscale value intervals correspond to different binarization thresholds; the grayscale value interval to which the representative grayscale value belongs is determined, and the binarization threshold corresponding to the grayscale value interval to which the representative grayscale value belongs is determined.

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