CN113313088A - Optimization method of side arc surface fingerprint data and fingerprint identification module - Google Patents
Optimization method of side arc surface fingerprint data and fingerprint identification module Download PDFInfo
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Abstract
The application provides an optimization method of side arc surface fingerprint data, which comprises the following steps: acquiring cambered surface fingerprint data of the template fingerprint by using a side cambered surface fingerprint sensor; acquiring a plurality of pixels from the arc fingerprint data and acquiring a first side length and a second side length according to a preset neighborhood; acquiring a first coefficient and a second coefficient according to the first side length, the second side length and the plurality of pixels; obtaining an optimization rule of the arc surface fingerprint data according to the first coefficient and the second coefficient, and obtaining the arc surface fingerprint data to be optimized; optimizing the cambered surface fingerprint data to be optimized into optimized plane fingerprint data by utilizing an optimization rule.
Description
Technical Field
The application relates to the field of touch electronic equipment, in particular to an optimization method of side arc surface fingerprint data and a fingerprint identification module.
Background
In order to improve security and user convenience, biometric systems, such as side fingerprint sensors, are increasingly used in mobile communication devices and portable tablet computers. The wide application of biometric identification systems is attributed to the small structure and stable performance of capacitive fingerprint chips, which are increasingly recognized by customers and widely popularized.
With the popularization of full screen technology and fingerprint identification technology in mobile terminals, the requirements for fingerprint identification modules are higher and higher on modern mobile portable devices, the size of the fingerprint identification module in a product is reduced while the identification performance and the user experience are improved, and side fingerprints realized on a mobile phone power key are widely accepted by users and manufacturers. Because side fingerprint sensor realizes on the power key, in order to compromise user's use and experience, some products make side fingerprint sensor outward appearance into pitch arc structure, this kind of structure has improved user's touching experience simultaneously, and the inevitable signal acquisition performance who has influenced capacitanc touch-control chip. In the characteristic of arc structure, the fingerprint distance module at both ends is far away, so capacitanc fingerprint is when the voltage difference of sensing electric capacity, and the fingerprint signal at both ends can be relatively more weak, and it is difficult to bring subsequent fingerprint matching identification.
Therefore, optimizing the arc fingerprint data acquired by the side arc fingerprint sensor is an urgent problem to be solved.
Disclosure of Invention
The application provides an optimization method and a fingerprint identification module of side cambered surface fingerprint data, which can optimize the quality of a side cambered surface fingerprint image, ensure the accuracy of a follow-up image identification comparison result, and realize a fingerprint matching function efficiently and accurately.
In a first aspect, an embodiment of the present application provides an optimization method for side arc fingerprint data, where the optimization method for side arc fingerprint data includes:
acquiring cambered surface fingerprint data of the template fingerprint by using a side cambered surface fingerprint sensor;
acquiring a plurality of pixels from the arc fingerprint data and acquiring a first side length and a second side length according to a preset neighborhood;
acquiring a first coefficient and a second coefficient according to the first side length, the second side length and the plurality of pixels;
obtaining an optimization rule of the cambered surface fingerprint data according to a first coefficient and a second coefficient, wherein the optimization rule adopts the following formula, G (i, j) = p (i, j) × f (i, j) + q (i, j), p (i, j) is the first coefficient and is also called a multiplicative coefficient, p (i, j) is the amplification capacity of the signal peak value of the side cambered surface fingerprint sensor at (i, j), i.e., the amplification amount, q (i, j) is a second coefficient, also called additive coefficient, q (i, j) is the offset of the signal mean value of the side arc fingerprint sensor at (i, j), namely offset, G (i, j) is optimized plane fingerprint data, f (i, j) is to-be-optimized cambered surface fingerprint data, namely, effective fingerprint signals of the side arc fingerprint sensor at (i, j), wherein (i, j) is the coordinate of a pixel;
acquiring cambered surface fingerprint data to be optimized;
optimizing the cambered surface fingerprint data to be optimized into optimized plane fingerprint data by utilizing an optimization rule.
In a second aspect, an embodiment of the present application provides a fingerprint identification module, and the fingerprint identification module includes:
a memory for storing program instructions;
and the processor is used for executing the program instruction so as to enable the fingerprint identification module to realize the optimization method of the side arc surface fingerprint data.
Third aspect, this application embodiment provides a fingerprint identification equipment, and this fingerprint identification equipment is provided with foretell fingerprint identification module.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which program instructions capable of being loaded by a processor and executing the above-mentioned optimization method for side arc fingerprint data are stored.
In a fifth aspect, an embodiment of the present application provides an electronic device, including:
a memory for storing program instructions; and
and the processor is used for executing the program instructions to enable the electronic equipment to realize the optimization method of the side arc surface fingerprint data.
According to the optimization method of the side arc surface fingerprint data, the side arc surface fingerprint sensor is used for obtaining the arc surface fingerprint data of the template fingerprint to obtain the optimization rule capable of converting the arc surface fingerprint data into the plane fingerprint data, and then the optimization rule is used for converting the arc surface fingerprint data to be optimized into the optimized plane fingerprint data. The quality of the side arc surface fingerprint image can be effectively improved, the accuracy of the fingerprint data of the follow-up image identification comparison result is guaranteed, and therefore the function of fingerprint matching is achieved efficiently and accurately.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is to be understood that the drawings in the following description are merely exemplary of the application and that other drawings may be derived from the structure shown in the drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 is a flowchart of a method for optimizing side arc fingerprint data according to a first embodiment of the present application.
Fig. 2 is a flowchart of a method for optimizing side arc fingerprint data according to a second embodiment of the present application.
Fig. 3 is a first sub-flowchart of a method for optimizing side arc fingerprint data according to a first embodiment of the present application.
Fig. 4 is a second sub-flowchart of the method for optimizing side arc fingerprint data according to the first embodiment of the present application.
Fig. 5 is a schematic diagram of arc fingerprint data provided in an embodiment of the present application.
Fig. 6 is a schematic diagram of arc fingerprint data processed by a first coefficient according to an embodiment of the present disclosure.
Fig. 7 is a schematic diagram of arc fingerprint data processed by a second coefficient according to an embodiment of the present disclosure.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 9 is a schematic diagram of a pixel neighborhood range of the optimization method for side arc fingerprint data according to the embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the descriptions in this application referring to "first", "second", etc. are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
Please refer to fig. 1, which is a flowchart illustrating a method for optimizing side arc fingerprint data according to a first embodiment of the present application. The method for optimizing the fingerprint data of the side arc surface provided by the first embodiment specifically comprises the following steps.
Step S101, a side arc fingerprint sensor is used to obtain arc fingerprint data of the template fingerprint, please refer to fig. 5, where fig. 5 is a signal of the arc fingerprint data.
And S102, acquiring a plurality of pixels from the arc fingerprint data and acquiring a first side length and a second side length according to a preset neighborhood. The predetermined neighborhood is a basic topology on the set. A certain range around each pixel is represented in the embodiments of the present application.
And step S103, acquiring a first coefficient and a second coefficient according to the first side length, the second side length and the plurality of pixels. The first coefficient and the second coefficient are used for optimizing pixels in the arc-surface fingerprint data into pixels in the plane fingerprint data, and the plane fingerprint data is equivalent to fingerprint data acquired by a plane fingerprint sensor. The first coefficient is used for adjusting the amplification capacity of the signal peak of the cambered surface fingerprint data, and the second coefficient is used for adjusting the deviation capacity of the cambered surface fingerprint data. The first coefficient and the second coefficient jointly adjust the cambered surface fingerprint data. Further, please refer to steps S301 to S303 and steps S401 to S403 for the specific steps of acquiring the first coefficient and the second coefficient.
And step S104, obtaining an optimization rule of the cambered surface fingerprint data according to the first coefficient and the second coefficient. In this embodiment, the side arc fingerprint sensor is affected by the surface curvature, and the vertical distances from the finger to different pixels of the sensor are changed when the image is acquired. Because the signal intensity collected by the sensor is in a negative correlation with the distance from the finger to the surface of the sensor, the fingerprint image collected by the side arc surface fingerprint sensor is obviously different from the signal peak value and the offset of different areas of the arc surface sensor, mainly the edge area and the central area, as shown in fig. 5, compared with the plane fingerprint sensor, wherein the edge of the sensor is thinner than the middle, the peak value and the average value of the signal are higher, and the difference can bring obvious influence on the processing and the enhancement of the later fingerprint image.
In this embodiment, the arc fingerprint data is optimized according to the optimization rule, so that the optimized arc fingerprint data is more convenient for post-processing of the fingerprint image. The optimization rule may be implemented by using the following formula, G (i, j) = p (i, j) × f (i, j) + q (i, j), where p (i, j) is a first coefficient, also referred to as a multiplicative coefficient, and further, p (i, j) may be understood as an amplification capability of a signal peak-to-peak value at (i, j) of the side arc fingerprint sensor, that is, an amplification coefficient, which may be obtained experimentally or obtained by a method described below. q (i, j) is a second coefficient, also called additive coefficient, and further q (i, j) can be understood as an offset of the signal mean value of the side arc fingerprint sensor at (i, j), that is, an offset, which can be obtained experimentally according to the coefficient or according to the method described below. G (i, j) is optimized plane fingerprint data, f (i, j) is cambered surface fingerprint data to be optimized, namely effective fingerprint signals of the side cambered surface fingerprint sensor at the position (i, j), and the (i, j) is coordinates of pixels, which can be acquired through the sensor. It can be understood that multiplicative coefficients and additive coefficients of the side arc fingerprint sensor collectively determine the optimization rules without considering noise.
And step S105, acquiring the fingerprint data of the cambered surface to be optimized. The data of the arc fingerprint to be optimized are obtained through the arc fingerprint sensor. The medium of the cambered surface fingerprint sensor for acquiring fingerprint data is a medium with the middle thickness larger than the thicknesses of the two sides.
And S106, optimizing the cambered surface fingerprint data to be optimized by utilizing an optimization rule to obtain optimized plane fingerprint data. And further, optimizing the plurality of pixels to be optimized into pixels corresponding to the plurality of planar fingerprint images by utilizing an optimization rule to obtain optimized arc fingerprint data. Referring to fig. 6 and fig. 7 in combination, the data of the arc fingerprint to be optimized after q (i, j) optimization is applied is changed from the graph as shown in fig. 6, and the data of fig. 6 after p (i, j) optimization is applied is changed as shown in fig. 7.
According to the optimization method of the side arc surface fingerprint data, the side arc surface fingerprint sensor is used for obtaining the arc surface fingerprint data of the template fingerprint to obtain the optimization rule capable of converting the arc surface fingerprint data into the plane fingerprint data, and then the optimization rule is used for converting the arc surface fingerprint data to be optimized into the optimized plane fingerprint data. The quality of the side arc surface fingerprint image can be effectively improved, the accuracy of the fingerprint data of the follow-up image identification comparison result is guaranteed, and therefore the function of fingerprint matching is achieved efficiently and accurately.
Please refer to fig. 2, which is a flowchart illustrating a method for optimizing side arc fingerprint data according to a second embodiment of the present application. The difference between the optimization method for the side arc fingerprint data provided by the second embodiment and the optimization method for the side arc fingerprint data provided by the first embodiment is that after the optimization rule is utilized to optimize the arc fingerprint data to be optimized into the optimized plane fingerprint data, the optimization method for the side arc fingerprint data provided by the second embodiment further comprises the following steps.
Step S201, determining a difference between the optimized planar fingerprint data and the template planar fingerprint data. The template planar fingerprint data is fingerprint data of a template fingerprint acquired by using a planar fingerprint sensor.
In step S202, if the difference is greater than or equal to the predetermined criterion, the first coefficient and the second coefficient are recalculated.
And S203, if the difference is smaller than a preset standard, using an optimization rule to optimize the arc surface fingerprint data to be optimized.
In this embodiment, by comparing the difference between the optimized planar fingerprint data and the template planar fingerprint data, it is determined whether the optimization rule can satisfy the current requirement for optimizing the arc fingerprint image, so as to facilitate the subsequent processing of the arc fingerprint image data.
Please refer to fig. 3, which is a flowchart illustrating the sub-steps of step S103 according to an embodiment of the present disclosure. Step S103 obtains a first coefficient and a second coefficient according to the first side length, the second side length, and the plurality of pixels, where the first coefficient is obtained, where obtaining the first coefficient specifically includes the following steps.
In step S301, pixel values of a plurality of pixels are acquired.
Step S302, summing the pixel values of the plurality of pixels to obtain a first intermediate value. In this embodiment, the sum of the ranges m and n of each pixel in the preset neighborhood is calculated to obtain a first intermediate value. Other ways of summing may be used in other embodiments.
Step S303, an averaging process is performed on the first intermediate value to obtain a first coefficient. In this embodiment, the first side length and the second side length are used to average each pixel. Other averaging methods may be used in other embodiments. In the present embodiment, the specific formula for obtaining the first coefficient p (i, j) is,
wherein, a is a first side length, b is a second side length, p (i, j) is a first coefficient, (i, j) is a coordinate of a current pixel, a and b represent the length and width of a neighborhood with a b, m, n represent the coordinate of a pixel with (i, j) in a neighborhood with a b, and F (m, n) represents the gray value of a pixel with (m, n) coordinate. Referring to fig. 9 in conjunction, the relationship between the coefficients is shown.
Please refer to fig. 4, which is a flowchart illustrating the sub-steps of step S103 according to an embodiment of the present disclosure. Step S103 obtains a first coefficient and a second coefficient according to the first side length, the second side length, and the plurality of pixels, where the first coefficient is obtained, and the obtaining of the second coefficient specifically includes the following steps.
In step S401, pixel values of a plurality of pixels are acquired.
In step S402, a second intermediate value is obtained by summing the pixel values of the plurality of pixels. In this embodiment, the sum of all pixels is calculated to obtain the second intermediate value. Other ways of summing may be used in other embodiments.
In step S403, the second intermediate value is averaged to obtain a second coefficient. In this embodiment, the first side length and the second side length are used to average each pixel. Other averaging methods may be used in other embodiments.
In the present embodiment, the specific formula for obtaining the first coefficient q (i, j) is,
wherein a is a first side length, b is a second side length, a and b represent the length and width of a neighborhood with a b, m, n represent the coordinates of the pixel with coordinates (i, j) in the neighborhood of a b, and F (m, n) represents the gray value of the pixel with coordinates (m, n).
In this embodiment, by comparing the optimized arc fingerprint data, the arc fingerprint data before optimization and the signal curve in fig. 7 and fig. 5, it can be seen that the peak-to-peak values and the offsets of the optimized arc fingerprint data at all points can be substantially consistent, so that the post-processing and enhancement of the image can be facilitated, and the preset optimization target is achieved.
The application also provides a fingerprint identification module, which comprises a memory and a processor, wherein the memory is used for storing program instructions; and the processor is used for executing the program instruction so that the fingerprint identification module realizes the optimization method of the side arc surface fingerprint data. Specifically, the fingerprint identification module is the core component of fingerprint lock, installs like on devices such as fingerprint entrance guard or hard disk for accomplish the module of the collection of fingerprint and the discernment of fingerprint. The fingerprint module mainly comprises a fingerprint acquisition module, a fingerprint identification module and an extended function module.
The application also provides a fingerprint identification device, and the fingerprint identification device is provided with foretell fingerprint identification module. For example, a portable electronic device such as a mobile phone or a tablet computer is provided with a side arc-shaped fingerprint sensor.
The present application also provides a computer-readable storage medium. The computer readable storage medium stores program instructions of the above-mentioned optimization method for side arc fingerprint data, which can be loaded and executed by a processor. Since the computer-readable storage medium adopts all the technical solutions of all the above embodiments, at least all the advantages brought by the technical solutions of the above embodiments are achieved, and no further description is given here.
The computer readable storage medium includes one or more program instructions. When loaded and executed on a device, cause the flow or functions according to embodiments of the application, in whole or in part. The apparatus may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The program instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the program instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The computer readable storage medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The present application further provides an electronic device 900, the electronic device 900 at least comprising a memory 901 and a processor 902. The memory 901 is used for storing program instructions of an optimization method of side arc fingerprint data. A processor 902 for executing program instructions to cause a computer device to implement the above-described method for optimizing side arc fingerprint data. Please refer to fig. 8, which is a schematic diagram of an internal structure of an electronic device 900 according to a first embodiment of the present application.
The memory 901 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 901 may be an internal storage unit of the electronic device 900, such as a hard disk of the electronic device 900, in some embodiments. The memory 901 may also be an external storage device of the electronic device 900 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital Card (SD), a Flash memory Card (Flash Card), etc., provided on the electronic device 900. Further, the memory 901 may also include both internal storage units and external storage devices of the electronic device 900. The memory 901 may be used to store not only application software installed in the electronic device 900 and various types of data, such as program instructions of an optimization method of side arc fingerprint data, but also temporarily store data that has been output or is to be output, such as data generated by execution of the optimization method of side arc fingerprint data.
Further, the electronic device 900 may further include a bus 903 which may be a Peripheral Component Interconnect (PCI) standard bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
Further, electronic device 900 may also include a display component 904. The display component 904 may be an LED (Light Emitting Diode) display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light Emitting Diode) touch panel, or the like. Display component 904 may also be referred to as a display device or display unit, as appropriate, for displaying information processed in electronic device 900 and for displaying a visual user interface, among other things.
Further, the electronic device 900 may also include a communication component 905, and the communication component 905 may optionally include a wired communication component and/or a wireless communication component (e.g., a WI-FI communication component, a bluetooth communication component, etc.), which are generally used to establish a communication connection between the electronic device 900 and other computer devices.
Fig. 8 only shows the electronic device 900 having the components 901 and 905 and program instructions for implementing the optimization method of side arc fingerprint data, and those skilled in the art will understand that the structure shown in fig. 8 does not constitute a limitation of the electronic device 900, and may include fewer or more components than those shown, or may combine some components, or may have different component arrangements. Since the electronic device 900 adopts all technical solutions of all the embodiments described above, at least all the beneficial effects brought by the technical solutions of the embodiments described above are achieved, and are not described herein again.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, to the extent that such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, it is intended that the present application also encompass such modifications and variations.
The above-mentioned embodiments are only examples of the present invention, and the scope of the claims of the present invention should not be limited by these examples, so that the claims of the present invention should be construed as equivalent and still fall within the scope of the present invention.
Claims (10)
1. A method for optimizing side arc fingerprint data is characterized by comprising the following steps:
acquiring cambered surface fingerprint data of the template fingerprint by using a side cambered surface fingerprint sensor;
acquiring a plurality of pixels from the arc fingerprint data and acquiring a first side length and a second side length according to a preset neighborhood;
acquiring a first coefficient and a second coefficient according to the first side length, the second side length and the plurality of pixels;
obtaining an optimization rule of the cambered surface fingerprint data according to the first coefficient and the second coefficient, wherein the optimization rule adopts the following formula, G (i, j) = p (i, j) × f (i, j) + q (i, j), p (i, j) is a first coefficient, also called multiplicative coefficient, p (i, j) is the amplification capacity, i.e. the amplification amount, of the signal peak value of the side cambered surface fingerprint sensor at (i, j), q (i, j) is a second coefficient, also called additive coefficient, q (i, j) is the offset of the signal mean value of the side cambered surface fingerprint sensor at (i, j), G (i, j) is optimized plane fingerprint data, f (i, j) is to-be-optimized cambered surface fingerprint data, i.e. the effective fingerprint signal of the side cambered surface fingerprint sensor at (i, j), (i, j) are coordinates of the pixel;
acquiring cambered surface fingerprint data to be optimized; and
and optimizing the cambered surface fingerprint data to be optimized into optimized plane fingerprint data by utilizing the optimization rule.
2. The method for optimizing side arc fingerprint data according to claim 1, wherein after optimizing the arc fingerprint data to be optimized into the optimized planar fingerprint data by using the optimization rule, the method further comprises:
judging the difference between the optimized planar fingerprint data and template planar fingerprint data, wherein the template planar fingerprint data is the fingerprint data of a template fingerprint acquired by a planar fingerprint sensor;
if the difference is greater than or equal to a preset standard, recalculating the first coefficient and the second coefficient; or
And if the difference is smaller than a preset standard, the optimization rule is used for optimizing the cambered surface fingerprint data to be optimized.
3. The method for optimizing side arc fingerprint data according to claim 1, wherein a first coefficient and a second coefficient are obtained according to the first side length, the second side length, and the plurality of pixels, wherein obtaining the first coefficient specifically includes:
acquiring pixel values of the plurality of pixels;
summing the pixel values of the plurality of pixels to obtain a first intermediate value; and
and averaging the first intermediate value to obtain the first coefficient.
4. The method for optimizing side arc fingerprint data according to claim 1, wherein a first coefficient and a second coefficient are obtained according to the first side length, the second side length, and the plurality of pixels, wherein obtaining the second coefficient specifically includes:
acquiring pixel values of the plurality of pixels;
summing the pixel values of the plurality of pixels to obtain a second intermediate value; and
and averaging the second intermediate value to obtain the second coefficient.
5. The method for optimizing side arc fingerprint data of claim 3, wherein the formula for obtaining the first coefficient specifically comprises:
wherein a is a first side length, b is a second side length, a and b represent the length and width of a neighborhood with a b, m, n represent the coordinates of the pixel with coordinates (i, j) in the neighborhood of a b, and F (m, n) represents the gray value of the pixel with coordinates (m, n).
6. The method for optimizing side arc fingerprint data of claim 4, wherein the formula for obtaining the second coefficient specifically comprises:
wherein a is a first side length, b is a second side length, a and b represent the length and width of a neighborhood with a b, m, n represent the coordinates of the pixel with coordinates (i, j) in the neighborhood of a b, and F (m, n) represents the gray value of the pixel with coordinates (m, n).
7. The utility model provides a fingerprint identification module, its characterized in that, the fingerprint identification module includes:
a memory for storing program instructions; and
a processor configured to execute the program instructions to enable the fingerprint identification module to implement the method for optimizing side arc fingerprint data according to any one of claims 1 to 6.
8. A fingerprint identification device, characterized in that fingerprint identification device is provided with a fingerprint identification module according to claim 7.
9. A computer-readable storage medium, wherein program instructions of the method for optimizing side arc fingerprint data according to any one of claims 1 to 6 are stored on the computer-readable storage medium and can be loaded and executed by a processor.
10. An electronic device, characterized in that the electronic device comprises:
a memory for storing program instructions; and
a processor configured to execute the program instructions to enable the electronic device to implement the method for optimizing side arc fingerprint data according to any one of claims 1 to 6.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0889432A2 (en) * | 1997-07-03 | 1999-01-07 | Fujitsu Limited | Roller fingerprint image capturing system |
| CN105550664A (en) * | 2016-01-08 | 2016-05-04 | 上海箩箕技术有限公司 | Optical fingerprint sensor module |
| KR20170033999A (en) * | 2015-09-18 | 2017-03-28 | 전창원 | Fingerprint recognition sensor and system improving sensing offset |
| CN107563170A (en) * | 2016-05-27 | 2018-01-09 | 广东欧珀移动通信有限公司 | Unlocked by fingerprint method and Related product |
| US20180018499A1 (en) * | 2015-02-13 | 2018-01-18 | Byd Company Limited | Method for calculating area of fingerprint overlapping region and electronic device thereof |
| CN109657656A (en) * | 2019-01-31 | 2019-04-19 | 北京集创北方科技股份有限公司 | Fingerprint detection method, finger print detection device and mobile terminal |
-
2021
- 2021-07-28 CN CN202110854726.1A patent/CN113313088B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0889432A2 (en) * | 1997-07-03 | 1999-01-07 | Fujitsu Limited | Roller fingerprint image capturing system |
| US20180018499A1 (en) * | 2015-02-13 | 2018-01-18 | Byd Company Limited | Method for calculating area of fingerprint overlapping region and electronic device thereof |
| KR20170033999A (en) * | 2015-09-18 | 2017-03-28 | 전창원 | Fingerprint recognition sensor and system improving sensing offset |
| CN105550664A (en) * | 2016-01-08 | 2016-05-04 | 上海箩箕技术有限公司 | Optical fingerprint sensor module |
| CN107563170A (en) * | 2016-05-27 | 2018-01-09 | 广东欧珀移动通信有限公司 | Unlocked by fingerprint method and Related product |
| CN109657656A (en) * | 2019-01-31 | 2019-04-19 | 北京集创北方科技股份有限公司 | Fingerprint detection method, finger print detection device and mobile terminal |
Non-Patent Citations (1)
| Title |
|---|
| 汤正刚: "自动指纹识别系统中的关键技术研究", 《中国优秀硕士论文全文数据库 信息科技辑》 * |
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