Fingerprint detection method, device and terminal equipment

Abstract

The application discloses a kind of fingerprint detection method, device and terminal equipment, comprising: according to the thickness of fingerprint sensing area, obtain multiple circuit parameters of acquisition circuit;According to multiple circuit parameters, multiple initial fingerprint images are collected;Multiple weighting coefficient matrices corresponding to each initial fingerprint image are provided;And based on multiple weighting coefficient matrices, multiple initial fingerprint images are superimposed to obtain target fingerprint image.The application obtains multiple initial fingerprint images by multiple groups of circuit parameters, and the quality of target fingerprint image better is obtained by superimposition calculation of multiple initial fingerprint images according to weighting value, without designing more complex acquisition circuit, the technical problem that the quality of fingerprint image is not good due to the uneven thickness of module of fingerprint detection device can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biometric recognition, and in particular to a fingerprint detection method, device and terminal equipment. BACKGROUND

[0002] With the popularity of terminal equipment, in order to protect personal privacy, biometric recognition technology applied in terminals has developed rapidly. And the display screen ratio is getting larger and larger, and currently the fingerprint detection device used for biometric recognition is usually arranged on the side of the terminal equipment. With the increasingly thin and light of various terminal equipment, the device thickness gradually decreases, and thus the sensing width of the fingerprint detection device located on the side of the terminal also decreases.

[0003] Currently, a fingerprint detection device with an arc-shaped surface is usually used to increase the contact area between the finger and the sensing surface in the case of reducing the width of the fingerprint detection device, so as to obtain more fingerprint information as much as possible, and thus improve the accuracy of fingerprint recognition.

[0004] However, the arc-shaped fingerprint detection device has the problem of uneven thickness, which greatly affects the noise in the obtained fingerprint image and reduces the fingerprint recognition accuracy. SUMMARY

[0005] In order to solve the above technical problems, the present application provides a fingerprint detection method, device and terminal equipment to improve the quality of the fingerprint image and thus improve the fingerprint recognition accuracy.

[0006] According to a first aspect of the present application, a fingerprint detection method is provided, comprising:

[0007] obtaining a plurality of circuit parameters of the acquisition circuit according to the thickness of the fingerprint sensing area;

[0008] acquiring a plurality of initial fingerprint images according to the plurality of circuit parameters;

[0009] obtaining a plurality of weighting coefficient matrices corresponding to each initial fingerprint image; and

[0010] superimposing the plurality of initial fingerprint images based on the plurality of weighting coefficient matrices to obtain a target fingerprint image.

[0011] Optionally, it further comprises:

[0012] dividing the fingerprint sensing area into a plurality of sub-areas.

[0013] Optionally, the step of superimposing the plurality of initial fingerprint images based on the plurality of weighting coefficient matrices to obtain a target fingerprint image comprises:

[0014] obtaining a plurality of groups of pixel values corresponding to each sub-area in the plurality of initial fingerprint images;

[0015] obtaining a plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices;

[0016] multiplying and superimposing each set of pixel values in the plurality of initial fingerprint images corresponding to each sub-region with the weighting coefficients corresponding thereto.

[0017] Optionally, the step of obtaining the weighting coefficient matrix corresponding to each initial fingerprint image comprises:

[0018] measuring the thickness of the fingerprint detection module of the plurality of sub-regions; and

[0019] based on the thickness of the fingerprint detection module of each sub-region and the plurality of circuit parameters, assigning a plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices, wherein the sum of the plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices is 1.

[0020] Optionally, the step of obtaining the weighting coefficient matrix corresponding to each initial fingerprint image comprises:

[0021] obtaining a test fingerprint image of a test object, wherein the test object is a flat and deformable object; and

[0022] based on the grayscale image of the test fingerprint image, assigning a plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices, wherein the sum of the plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices is 1.

[0023] Optionally, the step of obtaining the plurality of circuit parameters of the acquisition circuit according to the thickness of the fingerprint sensing region comprises:

[0024] the step of acquiring a plurality of initial fingerprint images according to the plurality of circuit parameters comprises: setting the acquisition circuit according to each circuit parameter and acquiring a corresponding one of the initial fingerprint images.

[0025] Optionally, the circuit parameters include an amplification coefficient and an offset value of the acquisition circuit.

[0026] According to another aspect of the present application, a fingerprint detection device is provided, comprising:

[0027] an encapsulation layer;

[0028] a coating layer covering the encapsulation layer and having a surface comprising a fingerprint sensing region, the coating layer having a plurality of thicknesses;

[0029] The fingerprint detection chip is packaged in the packaging layer and includes a capacitive fingerprint sensor and a processor. The capacitive fingerprint sensor acquires a plurality of sets of capacitive detection data based on a plurality of circuit parameters. The processor obtains the plurality of circuit parameters of the acquisition circuit according to the thickness of the fingerprint sensing area and provides the plurality of circuit parameters to the acquisition circuit of the capacitive sensor, and processes a plurality of sets of capacitive detection data to obtain a plurality of initial fingerprint images. The processor also obtains a plurality of weighting coefficient matrices corresponding to each initial fingerprint image, and superimposes the plurality of initial fingerprint images based on the plurality of weighting coefficient matrices to obtain a target fingerprint image.

[0030] Optionally, the fingerprint sensing area of the coating surface is divided into a plurality of sub-areas.

[0031] Optionally, the processor includes:

[0032] The processing unit obtains a plurality of sets of pixel values corresponding to each sub-area in the plurality of initial fingerprint images, and obtains a plurality of weighting coefficients corresponding to each sub-area in the plurality of weighting coefficient matrices; and

[0033] The first calculation unit multiplies and superimposes the plurality of sets of pixel values corresponding to each sub-area in the plurality of initial fingerprint images and the weighting coefficients corresponding thereto.

[0034] Optionally, the processor further includes:

[0035] The measurement unit controls the measurement of the thickness of the fingerprint detection module of the plurality of sub-areas; and

[0036] The second calculation unit assigns a plurality of weighting coefficients corresponding to each sub-area in the plurality of weighting coefficient matrices based on the thickness of the fingerprint detection module of each sub-area and the plurality of circuit parameters, wherein the sum of the plurality of weighting coefficients corresponding to each sub-area in the plurality of weighting coefficient matrices is 1.

[0037] Optionally, the processor further includes:

[0038] The first control unit controls the acquisition of a test fingerprint image of a test object, wherein the test object is a flat and deformable object; and

[0039] The third calculation unit assigns a plurality of weighting coefficients corresponding to each sub-area in the plurality of weighting coefficient matrices based on the grayscale image of the test fingerprint image, wherein the sum of the plurality of weighting coefficients corresponding to each sub-area in the plurality of weighting coefficient matrices is 1.

[0040] Optionally, the processor further includes:

[0041] The second control unit provides a plurality of circuit parameters to the capacitive fingerprint sensor based on the thickness of the fingerprint detection module of the plurality of sub-regions, and obtains the plurality of initial fingerprint images.

[0042] Optionally, the circuit parameters include an amplification coefficient and an offset value of the acquisition circuit.

[0043] According to still another aspect of the present application, a terminal device is provided, which comprises the above fingerprint detection apparatus.

[0044] The fingerprint detection method, apparatus and terminal device provided by the present application obtain a plurality of circuit parameters of an acquisition circuit according to the thickness of a fingerprint sensing region, acquire a plurality of initial fingerprint images based on the plurality of circuit parameters, obtain a plurality of weighting coefficient matrices corresponding to each initial fingerprint image, and superimpose the plurality of initial fingerprint images based on the plurality of weighting coefficient matrices to obtain a target fingerprint image, thereby solving the technical problem of poor fingerprint image quality caused by uneven module thickness of the fingerprint detection apparatus without designing a more complex acquisition circuit.

[0045] Further, the fingerprint detection chip, for example, based on the module thickness of the fingerprint detection apparatus, or the relationship between the different module thicknesses of the fingerprint detection apparatus and the signal amount sizes acquired thereby, and according to the circuit parameters of each initial fingerprint image, provides each initial fingerprint image with a corresponding weighting coefficient matrix, thereby making the generated target fingerprint image clearer, and thereby improving the fingerprint recognition accuracy.

[0046] It should be noted that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1a A structural schematic diagram of a fingerprint detection apparatus provided according to a first embodiment of the present application is shown;

[0048] Figure 1b A principle schematic diagram of fingerprint acquisition by the fingerprint detection apparatus provided according to the first embodiment of the present application is shown;

[0049] Figure 2 A flow schematic diagram of a fingerprint detection method provided according to a second embodiment of the present application is shown;

[0050] Figure 3 A flow schematic diagram of step S110 and step S120 in the fingerprint detection method provided according to the second embodiment of the present application is shown;

[0051] Figure 4 A flow schematic diagram of step S130 in the fingerprint detection method provided according to the second embodiment of the present application is shown;

[0052] Figure 5 Fig. 6 shows another flow chart of step S130 in the method for detecting fingerprint according to the second embodiment of the present application;

[0053] Figure 6 Fig. 7 shows a flow chart of step S140 in the method for detecting fingerprint according to the second embodiment of the present application;

[0054] Figure 7 Fig. 8 shows a schematic diagram of the principle of step S140 in the method for detecting fingerprint according to the second embodiment of the present application;

[0055] Figure 8 Fig. 3 shows a schematic diagram of one structure of the fingerprint detection chip in the fingerprint detection device according to the first embodiment of the present application;

[0056] Figure 9 Fig. 4 shows another schematic diagram of the fingerprint detection chip in the fingerprint detection device according to the first embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the present application more comprehensible, the present application will be described in more detail below with reference to the relevant drawings. The preferred embodiments of the present application are shown in the drawings. However, the present application can be realized in different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more comprehensive and thorough.

[0058] Figure 1a Fig. 1 shows a schematic diagram of the structure of the fingerprint detection device according to the first embodiment of the present application, Figure 1b Fig. 2 shows a schematic diagram of the principle of the fingerprint collection by the fingerprint detection device according to the first embodiment of the present application.

[0059] As Figure 1aAs shown, the fingerprint detection device includes a coating layer 110, a packaging layer 120, and a fingerprint detection chip 130. The coating layer 110 covers the packaging layer 120 and has a surface including a fingerprint sensing area (not shown in the figure), and the coating layer 110 has a plurality of thicknesses. Further, the surface of the coating layer 110 where the fingerprint sensing area is located is, for example, arc-shaped, and the thickness of the surface of the coating layer 110 decreases from the center to the edge. The fingerprint detection chip 130 is packaged in the packaging layer 120, and the packaging layer 120 is, for example, a molded part. The fingerprint detection chip 130 includes a capacitive fingerprint sensor 140 and a processor 150. The capacitive fingerprint sensor 140 sets an acquisition circuit inside based on a plurality of circuit parameters and acquires corresponding capacitive detection data of a plurality of initial fingerprint images. The processor 150 is configured to process the capacitive detection data of the initial fingerprint images to obtain a plurality of initial fingerprint images, provide a plurality of weighting coefficient matrices corresponding to each initial fingerprint image, and superimpose the plurality of initial fingerprint images based on the plurality of weighting coefficient matrices to obtain a target fingerprint image.

[0060] In combination Figure 1b As shown, when the finger 500 touches the fingerprint sensing area in the coating layer 110 of the fingerprint detection device, the thickness d of the coating layer 110 is not uniform, and thus the module thickness of the fingerprint detection device is not uniform, and thus the signal amount of the capacitive detection data acquired by the capacitive fingerprint sensor 140 is different due to the influence of the module thickness of the corresponding area.

[0061] In the fingerprint detection device of the present application, the fingerprint detection chip 130 acquires a plurality of initial fingerprint images based on a plurality of circuit parameters, provides a plurality of weighting coefficient matrices corresponding to each initial fingerprint image, and superimposes the plurality of initial fingerprint images based on the plurality of weighting coefficient matrices to obtain a target fingerprint image, so as to solve the technical problem of poor quality of the fingerprint image caused by the non-uniform module thickness of the fingerprint detection device.

[0062] Figure 2 A flowchart of a fingerprint detection method according to the second embodiment of the present application is shown. Figure 3 A flowchart of steps S110 and S120 in the fingerprint detection method according to the second embodiment of the present application is shown. Figure 4 A flowchart of step S130 in the fingerprint detection method according to the second embodiment of the present application is shown. Figure 5 Another flowchart of step S130 in the fingerprint detection method according to the second embodiment of the present application is shown. Figure 6 A flowchart of step S140 in the fingerprint detection method according to the second embodiment of the present application is shown. Figure 7 A schematic diagram of the principle of step S140 in the fingerprint detection method according to the second embodiment of the present application is shown.

[0063] AsFigure 2 As shown, the fingerprint detection method comprises the following steps:

[0064] Step S110: Obtain multiple circuit parameters of the acquisition circuit according to the thickness of the fingerprint sensing area.

[0065] Step S120: Collect multiple initial fingerprint images according to the multiple circuit parameters. The fingerprint detection chip 130 collects one corresponding initial fingerprint image based on each circuit parameter.

[0066] Step S130: Obtain multiple weighting coefficient matrices corresponding to each initial fingerprint image. The fingerprint detection chip 130, for example, provides one corresponding weighting coefficient matrix to each initial fingerprint image based on the module thickness of the fingerprint detection device, or the relationship between the different module thicknesses of the fingerprint detection device and the signal amount sizes collected thereby, and according to the circuit parameter of each initial fingerprint image.

[0067] Step S140: Superimpose the multiple initial fingerprint images based on the multiple weighting coefficient matrices to obtain a target fingerprint image. The fingerprint detection chip 130 superimposes the multiple initial fingerprint images at a certain proportion based on the multiple weighting coefficient matrices to obtain a target fingerprint image with higher quality.

[0068] In other embodiments, for example, the fingerprint sensing area of the detection device is further divided into multiple sub-areas. Further, in combination with Figure 3 As shown, step S110 comprises the following steps:

[0069] Step S111: Obtain multiple circuit parameters based on the thickness of the fingerprint detection module of each sub-area. The circuit parameters are, for example, amplification coefficients and offset values. Further, for example, the fingerprint detection area is divided into multiple sub-areas arranged in an array, and the thickness of the fingerprint detection module corresponding to each sub-area is measured by the processor 150 in the fingerprint detection chip 130. Further, the thickness of the fingerprint detection module in the sub-area is, for example, the average or maximum value of the thickness corresponding to all pixel points in the area. Further, for example, the thickness of the fingerprint detection module of each sub-area is divided into multiple groups according to the size, and for example, one circuit parameter is assigned based on the average or median or maximum value of the thickness of each group, which is, for example, the circuit parameter of the acquisition circuit that best matches the thickness (average or median or maximum value of the thickness of each group), and the initial fingerprint image collected under the circuit parameter of the acquisition circuit that best matches the thickness is the clearest image at the sub-area. Further, the thicker the thickness of the fingerprint detection module, the larger the amplification coefficient and the lower the offset value in the circuit parameter that best matches the sub-area.

[0070] Further, step S120 comprises the following steps:

[0071] Step S121: set the acquisition circuit of the fingerprint detection device according to each circuit parameter and acquire a corresponding initial fingerprint image. Specifically, the processor 150 in the fingerprint detection chip 130 provides each circuit parameter to the capacitive fingerprint sensor 140 to set the acquisition circuit in the capacitive fingerprint sensor 140 and acquire the capacitive detection data of the corresponding initial fingerprint image, and then the processor 150 processes the capacitive detection data of the initial fingerprint image to obtain a plurality of initial fingerprint images.

[0072] Further, in combination with Figure 4 As shown in FIG. 1, step S130 for example includes the following steps:

[0073] Step S131: measure the thickness of the fingerprint detection module in each sub-region. Further, the processor 150 in the fingerprint detection chip 130 is controlled to measure the thickness of the fingerprint detection module corresponding to each sub-region. Further, the thickness of the fingerprint detection module in the sub-region is for example the average or maximum value of the thickness corresponding to all pixel points in the region.

[0074] Step S132: based on the thickness of the fingerprint detection module in each sub-region and the plurality of circuit parameters, correspondingly assign a plurality of weighting coefficients to each sub-region in a plurality of weighting coefficient arrays. Specifically, the processor 150 correspondingly assigns a plurality of weighting coefficients to each sub-region in a plurality of weighting coefficient arrays based on the thickness of each sub-region and the circuit parameters corresponding to each initial fingerprint image. Further, the sum of the plurality of weighting coefficients of each sub-region in the plurality of weighting coefficient arrays is 1.

[0075] Further, for example, taking the case of providing two circuit parameters as an example, two initial fingerprint images are correspondingly acquired. The initial fingerprint image acquired based on the circuit parameter with a larger magnification factor can completely contain the fingerprint information of the thicker region in the module, while the fingerprint information of the thinner region in the module is missing due to image saturation. Similarly, the initial fingerprint image acquired based on the circuit parameter with a smaller magnification factor can completely contain the fingerprint information of the thinner region in the module, while the fingerprint information of the thicker region in the module is missing due to quantization error. Further, the weighting coefficient of the current region is determined according to which initial fingerprint image the current region is better represented in.

[0076] In other embodiments, in combination with Figure 5 As shown in FIG. 1, step S130 for example includes the following steps:

[0077] Step S231: Acquire a test fingerprint image of the test object. Specifically, the capacitive fingerprint sensor 140 collects capacitance detection data of the test object and processes it through the processor 150 to obtain a test fingerprint image. The test object is a flat and deformable material to provide full coverage of the fingerprint sensing area with pressure. The grayscale image of the test fingerprint then reflects the relationship between the thickness of different fingerprint detection modules and the signal strength. For example, in a fingerprint detection device with a curved surface, the signal strength in the central region of the grayscale image of the test fingerprint is smaller than that in the edge region.

[0078] Step S232: Based on the grayscale image of the test fingerprint image, assign multiple weighting coefficients to each sub-region in multiple weighting coefficient arrays. Specifically, the processor 150 assigns multiple weighting coefficients to each sub-region in multiple weighting coefficient arrays based on the grayscale image of the test fingerprint image. Further, the sum of the multiple weighting coefficients of each sub-region in the multiple weighting coefficient arrays is 1. Further, the initial fingerprint image acquired based on circuit parameters with a larger amplification factor can completely contain the fingerprint information with smaller signal intensity in the test fingerprint image, while the fingerprint information with larger signal intensity in the test fingerprint image will be missing due to image saturation. Similarly, the initial fingerprint image acquired based on circuit parameters with a smaller amplification factor can completely contain the fingerprint information with larger signal intensity in the test fingerprint image, while the fingerprint information with smaller signal intensity in the test fingerprint image will be missing due to quantization error.

[0079] Furthermore, combined Figure 6 , Figure 7 As shown, step S140 includes, for example, the following steps:

[0080] Step S141: Obtain multiple pixel values ​​corresponding to each sub-region in multiple initial fingerprint images. Specifically, the processor 150 obtains the pixel values ​​corresponding to each sub-region in multiple initial fingerprint images (P1-Pn). Taking the sub-region in the first row and first column of the fingerprint sensing area as an example, the processor 150 obtains the pixel values ​​in the sub-region located in the first row and first column of each initial fingerprint image until all pixel values ​​corresponding to each sub-region in each initial fingerprint image have been read.

[0081] Step S142: Obtain multiple weighting coefficients corresponding to each sub-region in multiple weighting coefficient matrices; specifically, the processor 150 obtains multiple weighting coefficients corresponding to each sub-region in multiple weighting coefficient matrices (A1-1n). Taking the sub-region in the first row and first column of the fingerprint sensing area as an example, the processor 150 obtains the weighting coefficients (W11-Wn1) of the sub-region located in the first row and first column of each weighting coefficient matrix, until all the weighting coefficients corresponding to each sub-region in each weighting coefficient matrix have been read.

[0082] Step S143: Multiply the pixel values of each sub-region in multiple initial fingerprint images by their corresponding weighting coefficients and then sum them up. For example, taking the pixel value of the sub-region at the first row and the first column of the target fingerprint image as an example, the pixel value of the sub-region at the first row and the first column of the target fingerprint image is equal to the product of the pixel value of the sub-region at the first row and the first column of the initial fingerprint image P1 multiplied by the weighting coefficient W11 of its corresponding weighting coefficient matrix A1, the product of the pixel value of the sub-region at the first row and the first column of the initial fingerprint image Pi (1 < i < n, where i is an integer and n is an integer greater than 1) multiplied by the weighting coefficient Wi1 of its corresponding weighting coefficient matrix Ai, and the product of the pixel value of the sub-region at the first row and the first column of the initial fingerprint image Pn multiplied by the weighting coefficient Wn1 of its corresponding weighting coefficient matrix An.

[0083] Figure 8 FIG. shows a schematic structural diagram of a fingerprint detection chip in a fingerprint detection device according to a first embodiment of the present invention.

[0084] As Figure 8 shown, the fingerprint detection chip 130 includes a capacitive fingerprint sensor 140 and a processor 150.

[0085] The capacitive fingerprint sensor 140 sets the acquisition circuit inside based on a plurality of circuit parameters and acquires a plurality of sets of capacitive detection data. The processor 150 includes a second control unit 151, a measurement unit 152, a second calculation unit 153, a processing unit 154, and a first calculation unit 155. The fingerprint sensing area of the coating surface is divided into a plurality of sub-areas. The second control unit 151 provides a plurality of circuit parameters to the capacitive fingerprint sensor 140 based on the thickness of the fingerprint detection module of the plurality of sub-areas, and processes a plurality of sets of capacitive detection data obtained from the capacitive fingerprint sensor 140 to obtain a plurality of initial fingerprint images. The measurement unit 152 controls the measurement of the thickness of the fingerprint detection module of the plurality of sub-areas. The second calculation unit 153 assigns a plurality of weighting coefficients to each sub-area in a plurality of weighting coefficient arrays based on the thickness of the fingerprint detection module of each sub-area obtained from the measurement unit 152 and the plurality of circuit parameters obtained from the second control unit 151. The sum of the plurality of weighting coefficients of each sub-area in the plurality of weighting coefficient arrays is 1. In the sub-area of the plurality of sub-areas whose thickness is greater than the first threshold value, the corresponding weighting coefficient in the plurality of initial fingerprint images is proportional to the corresponding circuit parameter; in the sub-area of the plurality of sub-areas whose thickness is less than or equal to the first threshold value, the corresponding weighting coefficient in the plurality of initial fingerprint images is inversely proportional to the corresponding circuit parameter, and the circuit parameter is the amplification coefficient of the acquisition circuit. The processing unit 154 obtains a plurality of sets of pixel values corresponding to each sub-area in the plurality of initial fingerprint images from the second control unit 151, and obtains a plurality of weighting coefficients corresponding to each sub-area in the plurality of weighting coefficient matrices from the second calculation unit 153. The first calculation unit 155 is connected to the processing unit 154 to multiply and superimpose the plurality of sets of pixel values of each sub-area in the plurality of initial fingerprint images with the corresponding weighting coefficients.

[0086] Figure 9 Another structure of a fingerprint detection chip in a fingerprint detection device according to the first embodiment of the present application is shown.

[0087] As Figure 9 shown, the fingerprint detection chip 230 includes a capacitive fingerprint sensor 240 and a processor 250.

[0088] The capacitive fingerprint sensor 240 sets the acquisition circuit inside based on a plurality of circuit parameters and acquires a plurality of sets of capacitive detection data. The processor 250 includes a second control unit 251, a first control unit 252, a third calculation unit 253, a processing unit 254, and a first calculation unit 255. The fingerprint sensing area of the coating surface is divided into a plurality of sub-areas. The second control unit 251 provides a plurality of circuit parameters to the capacitive fingerprint sensor 240 based on the thickness of the fingerprint detection module of the plurality of sub-areas, and processes a plurality of sets of capacitive detection data obtained from the capacitive fingerprint sensor 240 to obtain a plurality of initial fingerprint images. The first control unit 252 controls the capacitive fingerprint sensor 240 to obtain a test object and finally obtain a test fingerprint image, and the test object is, for example, a uniform and deformable object. The third calculation unit 253 processes the corresponding gray scale image based on the test fingerprint image obtained from the first control unit 252, and assigns a plurality of weighting coefficients to each sub-area in a plurality of weighting coefficient arrays based on the gray scale image of the test fingerprint image, wherein the sum of the plurality of weighting coefficients of each sub-area in the plurality of weighting coefficient arrays is 1. In the sub-area where the gray value of the plurality of sub-areas in the gray scale image of the test fingerprint image is greater than the second threshold, the corresponding weighting coefficient in the plurality of initial fingerprint images is inversely proportional to the corresponding circuit parameter; in the sub-area where the gray value of the plurality of sub-areas in the gray scale image of the test fingerprint image is less than or equal to the second threshold, the corresponding weighting coefficient in the plurality of initial fingerprint images is proportional to the corresponding circuit parameter, and the circuit parameter is the amplification coefficient of the acquisition circuit. The processing unit 254 obtains a plurality of sets of pixel values of each sub-area in the plurality of initial fingerprint images from the second control unit 251, and obtains a plurality of weighting coefficients of each sub-area in the plurality of weighting coefficient matrices from the third calculation unit 253. The first calculation unit 255 is connected to the processing unit 254 to multiply and superimpose the plurality of sets of pixel values of each sub-area in the plurality of initial fingerprint images with the corresponding weighting coefficients.

[0089] It should be noted that the fingerprint detection method of the present application is applied to a coating non-planar fingerprint detection device.

[0090] The present application also provides a terminal device, which at least includes the above-mentioned fingerprint detection device. The specific implementation of the terminal device can refer to the above description, which will not be repeated here. The terminal device is, for example, not limited to various mobile intelligent terminals, door locks, and automobile devices.

[0091] It should be noted that the numerical values in this paper are only used for illustrative description, and in other embodiments of the present application, other numerical values can also be used to implement the present scheme, and the specific setting should be reasonable according to the actual situation, and the present application does not limit this.

[0092] It should be noted that the above-mentioned embodiments are merely given as an example to illustrate the application and are not intended to limit the mode of implementation. Based on the above description, one of ordinary skill in the art can make further changes or modifications to the embodiments in different forms. Here, it is not necessary or possible to exhaust all the embodiments. The obvious changes or modifications derived therefrom are still within the scope of the application.

[0093] It should also be understood that the terms and expressions used herein are used only to describe and not to limit the one or more embodiments of the present disclosure. Use of such terms and expressions does not exclude any equivalents of the features shown and described (or part thereof), and it is recognized that various modifications are possible within the scope of the claims. Other modifications, changes, and substitutions are also possible. Accordingly, the claims are intended to cover all such equivalents.

Claims

1. A method of detecting a fingerprint, characterized by, The method comprises: obtaining a plurality of circuit parameters of the acquisition circuit according to the thickness of the fingerprint sensing area; acquiring a plurality of initial fingerprint images according to the plurality of circuit parameters; obtaining a plurality of weight coefficient matrices corresponding to each initial fingerprint image; and superimposing the plurality of initial fingerprint images based on the plurality of weight coefficient matrices to obtain a target fingerprint image, dividing the fingerprint sensing area into a plurality of sub-areas, wherein the step of superimposing the plurality of initial fingerprint images based on the plurality of weight coefficient matrices to obtain a target fingerprint image comprises: obtaining a plurality of groups of pixel values corresponding to each sub-area in the plurality of initial fingerprint images; obtaining a plurality of weight coefficients corresponding to each sub-area in the plurality of weight coefficient matrices; multiplying and superimposing the plurality of groups of pixel values of each sub-area in the plurality of initial fingerprint images with the weight coefficients corresponding thereto respectively. The step of obtaining a plurality of weight coefficient matrices corresponding to each initial fingerprint image comprises:

2. The method of claim 1, wherein, measuring the thickness of the fingerprint detection module of the plurality of sub-areas; and distributing a plurality of weight coefficients corresponding to each sub-area in the plurality of weight coefficient matrices based on the thickness of the fingerprint detection module of each sub-area and the plurality of circuit parameters, wherein the sum of the plurality of weight coefficients of each sub-area in the plurality of weight coefficient matrices is 1. The step of obtaining a plurality of weight coefficient matrices corresponding to each initial fingerprint image comprises:

3. The method of claim 1, wherein, obtaining a test fingerprint image of a test object, wherein the test object is a flat and deformable object; and distributing a plurality of weight coefficients corresponding to each sub-area in the plurality of weight coefficient matrices based on the grayscale image of the test fingerprint image, wherein the sum of the plurality of weight coefficients of each sub-area in the plurality of weight coefficient matrices is 1. The step of obtaining a plurality of circuit parameters of the acquisition circuit according to the thickness of the fingerprint sensing area comprises: obtaining a plurality of circuit parameters based on the thickness of the fingerprint detection module of the plurality of sub-areas, 4. The method of claim 1, wherein, The step of acquiring a plurality of initial fingerprint images according to a plurality of circuit parameters comprises: setting the acquisition circuit according to each circuit parameter and acquiring a corresponding one of the initial fingerprint images. The circuit parameters include the amplification coefficient and the offset value of the acquisition circuit.

5. The method of claim 1, wherein, The method comprises:

6. A fingerprint detection device, characterized by a packaging layer; a coating layer covering the packaging layer and having a surface comprising a fingerprint sensing area, the coating layer having a plurality of thicknesses, the fingerprint sensing area of the surface of the coating layer being divided into a plurality of sub-areas; a fingerprint detection chip packaged in the packaging layer, comprising a capacitive fingerprint sensor and a processor, the capacitive fingerprint sensor acquiring a plurality of groups of capacitive detection data based on a plurality of circuit parameters, the processor obtaining a plurality of circuit parameters of the acquisition circuit according to the thickness of the fingerprint sensing area and providing the acquisition circuit to the capacitive fingerprint sensor, and processing the plurality of groups of capacitive detection data to obtain a plurality of initial fingerprint images respectively, the processor further obtaining a plurality of weight coefficient matrices corresponding to each initial fingerprint image, and superimposing the plurality of initial fingerprint images based on the plurality of weight coefficient matrices to obtain a target fingerprint image, the processor comprising: ​ The processing unit obtains a plurality of sets of pixel values corresponding to each sub-region in the plurality of initial fingerprint images, and obtains a plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices; and The first calculation unit multiplies and superimposes the plurality of sets of pixel values corresponding to each sub-region in the plurality of initial fingerprint images with the weighting coefficients corresponding thereto respectively.

7. The fingerprint detection apparatus according to claim 6, wherein The processor further comprises: The measurement unit controls the measurement of the thickness of the fingerprint detection module of the plurality of sub-regions; and The second calculation unit assigns a plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices based on the thickness of the fingerprint detection module of each sub-region and the plurality of circuit parameters, wherein the sum of the plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices is 1.

8. The fingerprint detection apparatus according to claim 6, wherein The processor further comprises: The first control unit controls the acquisition of a test fingerprint image of a test object, wherein the test object is a flat and deformable object; and The third calculation unit assigns a plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices based on the grayscale image of the test fingerprint image, wherein the sum of the plurality of weighting coefficients corresponding to each sub-region in the plurality of weighting coefficient matrices is 1.

9. The fingerprint detection apparatus according to claim 6, wherein The processor further comprises: The second control unit provides a plurality of circuit parameters to the capacitive fingerprint sensor based on the thickness of the fingerprint detection module of the plurality of sub-regions, and obtains the plurality of initial fingerprint images.

10. The fingerprint detection apparatus according to claim 6, wherein The circuit parameters are amplification coefficients and offset values of an acquisition circuit.

11. A terminal device, comprising: The fingerprint detection device according to any one of claims 6-10.

Images