A method for adjusting image humidity based on interpolation algorithm

Abstract

A method for adjusting the dryness and wetness of an image based on an interpolation algorithm: 1. Define the pixels of the image acquisition unit, acquire a fingerprint image, establish the coordinates for image processing through interpolation operations, and determine the positions of multiple image acquisition units. 2. Perform image processing on multiple image acquisition units, and determine the fingerprint type based on the image processing results. If the fingerprint image is determined to be a neutral fingerprint image, output the fingerprint image; otherwise, proceed to step 3. 3. Based on the wetness and dryness adjustment parameter table, the main controller modifies the corresponding register parameters of the capacitive fingerprint sensor through the SPI interface to adjust the wetness and dryness of the fingerprint images of multiple image acquisition units. The traditional algorithm takes 120ms to acquire an entire image when adjusting the wetness and dryness. By reducing the acquisition area, this algorithm only takes 40ms to acquire an image, greatly speeding up the acquisition speed.

Description

Technical Field

[0001] The present invention relates to the field of capacitive fingerprint sensors, and in particular to an image dryness and wetness adjustment method based on an interpolation algorithm. Background Art

[0002] Although image processing algorithms related to adjustment processing already exist, the processing time is relatively long, and the use of grayscale calculations for the entire image will result in a significant waste of time. This small-area block interpolation operation can effectively reduce the time required for image grayscale processing, while also being adaptable to image acquisition of any input size, ensuring high accuracy. In addition, when the finger is wet, the image is blurred, and when the finger is dry, the image is light, with unclear and fewer lines. This can cause the feature generation algorithm to not operate properly and result in low recognition rates. In addition, the traditional fingerprint image wetness adjustment method involves collecting the entire fingerprint image and performing a wetness adjustment. The disadvantage of this method is that it is time-consuming, the fingerprint recognition time is relatively long, and users experience slow recognition speed and a poor user experience. Summary of the Invention

[0003] In order to solve the problems of traditional fingerprint image wetness adjustment method, which involves collecting the entire fingerprint image and adjusting the wetness, resulting in a long time consumption, a long fingerprint recognition time, slow recognition speed and poor user experience, the present invention adopts the following technical solutions:

[0004] A method for adjusting image dryness and wetness based on an interpolation algorithm includes the following steps.

[0005] Step S1: define the pixels of the image acquisition unit, collect the fingerprint image, and determine the positions of multiple image acquisition units through interpolation operation;

[0006] Step S2: Perform image processing on multiple image acquisition units and determine the fingerprint type based on the image processing results. If the fingerprint image is determined to be a dry fingerprint image or a wet fingerprint image, proceed to step S3; if the fingerprint image is determined to be a normal fingerprint image, output the fingerprint image;

[0007] Step S3: For dry fingerprint images or wet fingerprint images, the main controller modifies the corresponding register parameters of the capacitive fingerprint sensor through the SPI interface to adjust the dryness and wetness of the fingerprint images of multiple minimum effective image acquisition units.

[0008] Specifically, the pixel size of the image acquisition unit is 32×32 pixels. After multiple tests, 32×32 pixels is the most suitable and minimum effective image acquisition unit area.

[0009] Specifically, in step S1, the process of establishing the capacitive fingerprint sensor coordinate diagram through interpolation is as follows: a row with a width of 32 pixels is removed along the top surface of the fingerprint image, and a column with a width of 32 pixels is removed along the right side of the fingerprint image. The remaining fingerprint image is then divided into 4×4 equal parts, and a capture unit is set at 1 / 4 of the length and 1 / 4 of the width; 3 / 4 of the length and 1 / 4 of the width; 1 / 4 of the length and 3 / 4 of the width; 3 / 4 of the length and 3 / 4 of the width; and 1 / 2 of the length and 1 / 2 of the width. This removal of rows and columns in the fingerprint image ensures that no matter where a capture unit is taken within the remaining fingerprint image, the image will not exceed its boundaries and can be adjusted to accommodate images of any area. This interpolation method effectively and evenly distributes the remaining fingerprint image at locations where effective image capture is possible.

[0010] Furthermore, a capture unit is set at the position of 1 / 4 of the length and 1 / 4 of the width of the remaining fingerprint image; 3 / 4 of the length and 1 / 4 of the width; 1 / 4 of the length and 3 / 4 of the width; 3 / 4 of the length and 3 / 4 of the width; and 1 / 2 of the length and 1 / 2 of the width, and the lower left corners of the five capture units correspond to each position.

[0011] Specifically, in step S2, the fingerprint image is processed, and the average grayscale value of the five image acquisition units is calculated. The average grayscale value and the reference grayscale value are interpolated and divided by the adjustment constant to obtain the grayscale feature value. Compared with directly judging the wetness of the fingerprint image by the average grayscale, the grayscale value of the fingerprint image is corrected by setting the adjustment constant and the reference grayscale value, so that the next step of fingerprint image type determination is more accurate.

[0012] If the grayscale eigenvalue is less than or equal to the first grayscale threshold, the fingerprint image is an extremely wet fingerprint image; if the grayscale eigenvalue is greater than the first grayscale threshold and less than the second grayscale threshold, the fingerprint image is a wet fingerprint image; if the grayscale eigenvalue is greater than or equal to the second grayscale threshold and less than or equal to the third grayscale threshold, the fingerprint image is a neutral fingerprint image; if the grayscale eigenvalue is greater than the third grayscale threshold and less than the fourth grayscale threshold, the fingerprint image is a wet fingerprint image; if the grayscale eigenvalue is greater than or equal to the fourth grayscale threshold, the fingerprint image is an extremely dry fingerprint image.

[0013] Specifically, in step S3, the register parameters include a contrast parameter and a background brightness parameter.

[0014] Specifically, in step S3, the process of adjusting the dryness and wetness of the fingerprint image is as follows:

[0015] Step S31: When the capacitive fingerprint sensor is initialized, the register parameter is preset to a neutral level. If the fingerprint image is an extremely dry fingerprint image or an extremely wet fingerprint image, the process proceeds to step S32; if the fingerprint image is a dry fingerprint image or a wet fingerprint image, the process proceeds to step S33;

[0016] Step S32: Set the adjustment step value to 2, adjust the register parameters according to the dry 2 level or wet 2 level parameters according to the dry and wet adjustment parameter table, and proceed to step S34;

[0017] Step S33: The adjustment step value is 1, and the register parameters are adjusted according to the dry 1 level or wet 1 level parameters according to the dry and wet adjustment parameter table, and then the process goes to step S34;

[0018] Step S34: increase the number of adjustments by 1; determine whether the number of adjustments exceeds the adjustment number threshold; if not, repeat steps S2 to S3 until the number of adjustments exceeds the adjustment number threshold; otherwise, end the adjustment and directly output the fingerprint image.

[0019] In summary, the device of the present invention has the following advantages: the traditional algorithm takes 120ms to capture a whole image during dry-wet adjustment. By reducing the acquisition area, the algorithm only takes 40ms to capture the image, greatly speeding up the acquisition speed. Figure 3 The wet finger image shown cannot pass the feature point generation algorithm smoothly, but the adjusted image can. Figure 5 The same is true for the dry finger image shown, which improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flowchart of an image dryness and humidity adjustment method based on an interpolation algorithm;

[0021] Figure 2 It is the fingerprint pattern of an extremely wet finger;

[0022] Figure 3 This is the fingerprint image after running this algorithm on an extremely wet finger;

[0023] Figure 4 It is a fingerprint diagram of an extremely dry finger;

[0024] Figure 5 This is the fingerprint image after running this algorithm on an extremely dry finger;

[0025] Figure 6 This is the schematic diagram of the capacitive fingerprint sensor control circuit;

[0026] Figure 7 This is the main control circuit schematic diagram of the capacitive fingerprint sensor;

[0027] Figure 8This is the clock schematic diagram of the main control circuit of the capacitive fingerprint sensor. DETAILED DESCRIPTION

[0028] The following combination Figure 1 and Figure 8 The present invention is further described.

[0029] A method for adjusting image dryness and wetness based on an interpolation algorithm includes the following steps.

[0030] Step S1: Define the pixel size of the image acquisition unit as 32×32 pixels, and use a 120×104 pixel capacitive fingerprint sensor to capture a fingerprint image. Interpolation is then used to establish image processing coordinates and determine the positions of multiple image acquisition units. The process of establishing the capacitive fingerprint sensor coordinate diagram through interpolation is as follows: remove a row with a width of 32 pixels along the top surface of the fingerprint image, remove a column with a width of 32 pixels along the right side of the fingerprint image, divide the remaining fingerprint image into 4×4 equal parts, and set a image acquisition unit at each of the five positions of the remaining fingerprint image: 1 / 4 of the length and 1 / 4 of the width; 3 / 4 of the length and 1 / 4 of the width; 1 / 4 of the length and 3 / 4 of the width; 3 / 4 of the length and 3 / 4 of the width; 3 / 4 of the length and 3 / 4 of the width; and 1 / 2 of the length and 1 / 2 of the width. The lower left corners of the five image acquisition units coincide with each position.

[0031] Step S2: perform image processing on the fingerprint image and calculate the average grayscale value of the five image acquisition units in step S1, and divide the average grayscale value and the reference grayscale value by the adjustment constant through interpolation to obtain the grayscale feature value.

[0032] If the grayscale eigenvalue is less than or equal to the first grayscale threshold, which is 40, the fingerprint image is an extremely wet fingerprint image; if the grayscale eigenvalue is greater than the first grayscale threshold and less than the second grayscale threshold, which is 80, the fingerprint image is a wet fingerprint image; if the grayscale eigenvalue is greater than or equal to the second grayscale threshold and less than or equal to the third grayscale threshold, which is 160, the fingerprint image is a neutral fingerprint image; if the grayscale eigenvalue is greater than the third grayscale threshold and less than the fourth grayscale threshold, which is 200, the fingerprint image is a wet fingerprint image; if the grayscale eigenvalue is greater than or equal to the fourth grayscale threshold, the fingerprint image is an extremely dry fingerprint image.

[0033] Step S3: For a dry fingerprint image or a wet fingerprint image, the main controller modifies the corresponding register parameters of the capacitive fingerprint sensor through the SPI interface, wherein the register parameters include contrast parameters and background brightness parameters. The process of adjusting the dryness and wetness of the fingerprint image is as follows:

[0034] Step S31: When the capacitive fingerprint sensor is initialized, the register parameter is preset to any neutral level. If the fingerprint image is an extremely dry fingerprint image or an extremely wet fingerprint image, then the process proceeds to step S32; if the fingerprint image is a dry fingerprint image or a wet fingerprint image, then the process proceeds to step S33;

[0035] Step S32: Set the adjustment step value to 2, adjust the register parameters according to the dry 2 level or wet 2 level parameters according to the dry and wet adjustment parameter table, and proceed to step S34;

[0036] Step S33: The adjustment step value is 1. According to the dry-wet adjustment parameter table in Table 1, the register parameters are adjusted according to the dry level 1 or wet level 1 parameters, and the process proceeds to step S34;

[0037] Step S34: increase the number of adjustments by 1; determine whether the number of adjustments exceeds the adjustment number threshold, which is 3. If not, repeat steps S2 to S3 until the number of adjustments exceeds the adjustment number threshold; otherwise, end the adjustment and directly output the fingerprint image.

[0038] Table 1. Dry and wet adjustment parameters

[0039]

[0040] The entire control device consists of a capacitive fingerprint sensor control circuit and a main control circuit, such as Figures 6 to 8 shown. Figure 6 U1 is a capacitive fingerprint sensor, which is connected via the SPI interface ( Figure 6 Pins 5 to 9 of the UI components represent the RST, MOSI, CS, CLK, and MISO signals of the SPI, respectively. Figure 7 U3 (SPI interface network identification and Figure 6 The main control MCU (U3) reads the fingerprint image through the SPI interface and runs the "fast dry and wet adjustment algorithm of interpolation operation" (processing flow chart as shown in the figure). Figure 1 ) Determine the wetness of the fingerprint image. If wetness adjustment is required, write the register parameters calculated by the software adjustment algorithm to the background and contrast gain registers of the capacitive fingerprint sensor through the SPI interface to perform wetness adjustment.

[0041] The basic principle of fingerprint image wet / dry adjustment is to compare the average grayscale value of 5 small-area fingerprint block images with the first grayscale threshold and the second grayscale threshold. If it is too wet or too dry, the background and contrast gain register parameters of the capacitive fingerprint sensor are adjusted. The register parameters can adjust parameters such as the internal signal amplification gain and capacitor charging voltage of the capacitive fingerprint sensor to return the image grayscale value to the normal range or close to the normal range, thereby improving image quality.

[0042] From the above description, it can be seen that the device has the following advantages: the traditional algorithm takes 120ms to capture a whole image when adjusting the dry and wet areas. By reducing the acquisition area, the algorithm only takes 40ms to capture the image, which greatly speeds up the acquisition speed. Figure 2 The wet finger image shown cannot pass the feature point generation algorithm smoothly, but the adjusted image can. Figure 4 The same is true for the dry finger image shown, which improves the user experience of dry finger users.

[0043] It is understood that the above specific description of the present invention is only used to illustrate the present invention and is not limited to the technical solutions described in the embodiments of the present invention. Those skilled in the art should understand that the present invention can still be modified or replaced with equivalents to achieve the same technical effects; as long as the use requirements are met, they are all within the scope of protection of the present invention.

Claims

1. A method for adjusting image humidity based on an interpolation algorithm, characterized in that: The following steps are involved: Step S1: defining pixels of an image acquisition unit, acquiring a fingerprint image, removing a row of pixels of a target width along the top surface of the fingerprint image, and removing a column of pixels of a target width along the right side of the fingerprint image; The remaining fingerprint image is divided into 4×4 evenly, and a sampling unit is set at the position of 1 / 4 of the length and 1 / 4 of the width of the remaining fingerprint image; the position of 3 / 4 of the length and 1 / 4 of the width; the position of 1 / 4 of the length and 3 / 4 of the width; the position of 3 / 4 of the length and 3 / 4 of the width; and the position of 1 / 2 of the length and 1 / 2 of the width. The lower left corners of the five sampling units coincide with each position. Step S2: performing image processing on the plurality of image acquisition units, calculating the average grayscale value of the five image acquisition units, and dividing the average grayscale value by an adjustment constant through interpolation to obtain a grayscale characteristic value; If the grayscale eigenvalue is less than or equal to the first grayscale threshold, the fingerprint image is an extremely wet fingerprint image; if the grayscale eigenvalue is greater than the first grayscale threshold and less than the second grayscale threshold, the fingerprint image is a wet fingerprint image; if the grayscale eigenvalue is greater than or equal to the second grayscale threshold and less than or equal to the third grayscale threshold, the fingerprint image is a neutral fingerprint image; if the grayscale eigenvalue is greater than the third grayscale threshold and less than the fourth grayscale threshold, the fingerprint image is a wet fingerprint image; if the grayscale eigenvalue is greater than or equal to the fourth grayscale threshold, the fingerprint image is an extremely dry fingerprint image; Wherein, when the fingerprint image is determined to be a neutral fingerprint image, the fingerprint image is output, otherwise the process goes to step S3; Step S3: According to the type of fingerprint image and in combination with the dryness and wetness adjustment parameter table, the main controller modifies the corresponding register parameters of the capacitive fingerprint sensor through the SPI interface to adjust the dryness and wetness of the fingerprint images of the multiple image acquisition units.

2. The image dryness and wetness adjustment method based on the interpolation algorithm according to claim 1, characterized in that: The pixel size of the image acquisition unit is 32×32 pixels.

3. The image dryness and wetness adjustment method based on interpolation algorithm according to claim 1, characterized in that: In step S3, the register parameters include a contrast parameter and a background brightness parameter.

4. The image dryness and wetness adjustment method based on interpolation algorithm according to claim 1, characterized in that: In step S3, the process of adjusting the dryness and wetness of the fingerprint image is as follows: Step S31: When the capacitive fingerprint sensor is initialized, the register parameter is preset to a neutral level. If the fingerprint image is an extremely dry fingerprint image or an extremely wet fingerprint image, the process proceeds to step S32; if the fingerprint image is a dry fingerprint image or a wet fingerprint image, the process proceeds to step S33; Step S32: setting the adjustment step value to 2, adjusting the register parameters according to the dry 2 level or wet 2 level parameters according to the dry and wet adjustment parameter table, and proceeding to step S34; Step S33: The adjustment step value is 1, and the register parameter is adjusted according to the dry 1 level or wet 1 level parameter according to the dry and wet adjustment parameter table, and then the process goes to step S34; Step S34: increase the number of adjustments by 1; determine whether the number of adjustments exceeds the adjustment number threshold; if not, repeat steps S2 to S3 until the number of adjustments exceeds the adjustment number threshold; otherwise, end the adjustment and directly output the fingerprint image.

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