A method for operating a high-precision touch control device

Abstract

The present invention relates to an operation method of a high-precision touch control device, which solves the technical problem of low precision. The method adopts step 1 to define the touch screen as a grid, adopts an interlaced scanning method to scan the touch area, and collects touch signals; step 2 to perform pixel optimization processing on the fingerprint image, and completes the pixel optimization processing through a pixel optimization processing function; step 3 to calculate the complex direction field of the fingerprint image after pixel optimization processing, and adopts a symmetric filter to perform filter processing, and uses the maximum corresponding pixel point of the processed field image as the center point; step 4 to perform effective image cropping with the center point of the fingerprint image to reduce the image area; step 5 to collect fingerprint features of the effective fingerprint recognition image, perform touch fingerprint recognition, and output the fingerprint recognition result and the center point parameter. The technical solution solves the problem well and can be used in high-precision touch control.

Description

Technical Field

[0001] The present invention relates to the field of high-precision touch control, and in particular to an operating method of a high-precision touch control device. Background Art

[0002] Touch Panel, also known as "touch screen" or "touch panel", is an inductive liquid crystal display device that can receive input signals such as contacts. When the graphic button on the screen is touched, the tactile feedback system on the screen can drive various connection devices according to the pre-programmed program. It can be used to replace the mechanical button panel and create vivid audio and video effects through the liquid crystal display screen. As the latest computer input device, the touch screen is a simple, convenient and natural way of human-computer interaction. It gives multimedia a new look and is a very attractive new multimedia interactive device. It is mainly used in public information query, industrial control, electronic games, multimedia teaching, etc.

[0003] The existing touch screen has the problem of poor control accuracy. The present invention solves the problem through an operation method of a high-precision touch control device. Summary of the invention

[0004] The technical problem to be solved by the present invention is the poor control accuracy in the prior art. A new operation method of a high-precision touch control device is provided, and the operation method of the high-precision touch control device has the characteristics of high accuracy.

[0005] In order to solve the above technical problems, the technical solutions adopted are as follows:

[0006] A method for operating a high-precision touch control device, the method comprising:

[0007] Step 1: define the touch screen as a grid, scan the touch area using an interlaced scanning method, and collect touch signals;

[0008] Step 2: Optimize the fingerprint image pixel by pixel optimization function. The pixel optimization function is -g(x,y)=exp{F -1 {G ln (u,v)}}:

[0009] Among them, G ln (u,v)=F ln (u,v)×H(u,v)=F i,ln (u,v)×H(u,v)+F r,ln (u,v)×H(u,v)=G i,ln (u,v)+G r,ln (u,v); filter function

[0010] F ln (u,v)=F[lni(x,y)+lnr(x,y)]=F i,ln (u,v)+F r,ln (u,v); F -1 () is the inverse Fourier transform function; F i,ln (u,v) is the preset lighting function, F r,ln (u, v) is the preset reflection function, M, N are the preset size parameters of the fingerprint collection image, 1≤u≤M, 1≤v≤N; high frequency gain coefficient γ H >1, low frequency gain coefficient γ L <1, D0≥3 is the cutoff frequency, c is the sharpening constant, γ H ≥c≥γ L ; ln() is a logarithmic function; i(x,y) is the total amount of light incident on the fingerprint collection scene, r(x,y) is the total amount of light reflected by the fingerprint, x is the x-axis coordinate of the pixel, and y is the y-axis coordinate of the pixel;

[0011] Step 3: Calculate the complex direction field of the fingerprint image after pixel optimization processing By using the symmetric filter h=(x+iy)g(x,y), we can calculate R(x,y)=||[(x+iy)g(x,y)]*z(x,y)||, and take the pixel point (x, y) corresponding to the maximum value of R(x,y) as the center point; where f x and f y represents the gradient of the preprocessed fingerprint image in the x and y directions, g(x,y)=exp(-(x 2 +y 2 ) / (2σ 2 )), σ is the preset coefficient value;

[0012] Step 4: Select any pixel point P(x,y), set a 3×3 window centered at P(x,y), define P as the ridge and valley value of the pixel point P(x,y), 1 represents the fingerprint ridge, and 0 represents the fingerprint valley; the remaining windows in the 3×3 window are pixel sequences P0, P1, ... P i ...P7;

[0013] If the 3×3 window satisfies the logical rule 2≤N(P(x,y))≤6and(T(P)=1)and(P0P2P4=0orT(P)≠0)and(P2P4P6=0orT(P)≠0), then the corresponding pixel point P(x,y) is eliminated, and all the pixels in the image are traversed to obtain the final fingerprint recognition area I(x,y);

[0014] in, T(P) represents the pixel sequence P0, P1, ... P in the 3×3 window of P i ...the number of changes from 0 to 1 in P7, P0P2P4 and P2P4P6 represent the logical products of each other;

[0015] Step 5: Collect fingerprint features of the effective fingerprint recognition image I(x, y), perform touch fingerprint recognition, and output the fingerprint recognition result and center point parameters.

[0016] Working principle of the present invention: The existing signal graph collected by the touch screen has unclear fingerprint images, and the technical problem of high error in direct positioning and fingerprint recognition is solved. The present invention uses a pixel optimization processing function to complete pixel optimization processing, eliminates invalid untouched touch screen signal areas, and then performs fingerprint recognition and positioning, eliminating the error probability, thereby improving the recognition accuracy and efficiency of the touch screen.

[0017] In the above scheme, for optimization, further, the interlaced scanning method includes:

[0018] Step 1.1, define any corner point (x1, y1) of the touch screen grid as the origin, define the search step length as L, start from the origin, search along the x direction, if a point is detected to exceed the threshold, it is defined as a touch point, and the position and value of the point are recorded and numbered in sequence, otherwise continue searching;

[0019] Step 1.2, update the point (x1, y1+N*L) as the origin, and return to step 1.1 until both the x direction and the y direction are searched, completing the preliminary positioning search, where N is an integer;

[0020] Step 1.3, take out the touch points one by one, update the touch points taken out this time as the origin, update the search step size to L / 2, search in sequence along the x direction, do not search the points that have been searched before, and automatically halve the search step size if the search is out of range, and continue searching until the step size is reduced to 1. If a new touch point appears during the search process, it is defined as a new point that needs to be searched in the y direction, and execute step 1.4, otherwise execute step 1.5;

[0021] Step 1.4: The search step length is L / 2 and remains unchanged. Search is performed in sequence along the y direction. The previously searched points are no longer searched. If the search is out of range, the search step length is automatically halved. The search is continued until the step length is reduced to 1. If a new touch point appears during the search process, it is defined as a new point that needs to be searched in the x direction. Step 1.4 is executed. Otherwise, step 1.5 is executed.

[0022] Step 1.5, until there are no new points to be retrieved, the retrieval is terminated, and the retrieved touch points are set as the effective area of ​​the fingerprint image contour map.

[0023] Furthermore, the touch fingerprint recognition includes:

[0024] Step 5.1, draw concentric circles with the center point as the center, divide the fingerprint image into B annular areas, and finally divide each annular area into K sector areas, where K and B are both predefined constants;

[0025] Step 5.2, calculate each sector S sq The sector fingerprint characteristic value V sqθ As Code1;

[0026]

[0027] Among them, F sqθ (x,y) is the fan-shaped area S sq The gray value of each pixel, P sqθ Represents the sector area S sq The average gray value of the inner pixels, n sq The annular area S sq The number of inside, 0<sq≤B×K-1,θ={0°,(360° / K),2*(360° / K),3*(360° / K),...≤180°}

[0028] Step 5.3, after rotating the fingerprint image (180° / K), repeat step 8.2 to extract each sector S sq The sector fingerprint characteristic value V sqθ As Code2;

[0029] Step 5.4, rotate Code1 and Code2 by R×(360° / K) (R=0,1,2...K-1) to obtain Code1' and Code2';

[0030] Step 5.5, input Code1 and Code2, Code1' and Code2' in step 5.4 into the historical fingerprint library for matching calculation;

[0031] Step 5.6, output the fingerprint recognition result.

[0032] Furthermore, the touch fingerprint recognition may also include:

[0033] Step A: define the fingerprint feature image to be matched and verified in the historical fingerprint library as the reference image I C , the collected target image to be matched is Define reference image I CAnd the target image after polar coordinate transformation The related relationships are as follows:

[0034] Among them, α z is the scale offset parameter, is the rotation offset parameter;

[0035] Step B, calculate the reference image I C Radial projection in polar coordinates Target image Projection in the radial direction K C (i) and Taking the logarithm gives LK C (i) and LK C (i) and The translation difference is used as the scale offset parameter α z ;

[0036]

[0037]

[0038]

[0039] K i =K max The number of samples in the angle direction, ce() represents the smallest integer greater than or equal to the value in the brackets, and fl() represents the largest integer less than or greater than the value in the brackets; the size of the effective fingerprint recognition image I(x,y) is 2K max ×2K max , n r =K max is the number of radial sampling, is the number of sampling in the angular direction;

[0040] Step C, calculate the reference image I according to the scale offset parameter in step B C and the target image Radial and angular projections:

[0041]

[0042]

[0043] right and Perform normalization calculation to calculate the translation of the highest point according to Calculate the rotation offset parameters

[0044] Step D, the rotation offset parameter φ z and scale shift parameter α z Bring in step A to correct the target image, and according to Calculate ∈ z The location point corresponding to the minimum value is the center point of the target image;

[0045] Step D: The target image after image correction When the error rate is less than the preset threshold after comparison with the reference image, the fingerprint recognition is defined as successful.

[0046] Beneficial effects of the present invention: The present invention uses a pixel optimization processing function to complete pixel optimization processing, and then performs fingerprint recognition and positioning, thereby improving the recognition accuracy of the touch screen. The interlaced scanning method changes the current row-by-row and column-by-column scanning, thereby improving efficiency. At the same time, by correcting the offset of the target fingerprint image, the number of fingerprints in the fingerprint library is reduced, and high-efficiency and high-precision fingerprint recognition and touch positioning are completed. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0048] Figure 1 , schematic diagram of the operation method of the high-precision touch control device. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] Example 1

[0051] This embodiment provides an operating method of a high-precision touch control device, such as Figure 1 , the operating method of the high-precision touch control device includes:

[0052] Step 1: define the touch screen as a grid, scan the touch area using an interlaced scanning method, and collect touch signals;

[0053] Step 2: Optimize the fingerprint image pixel by pixel optimization function. The pixel optimization function is -g(x,y)=exp{F -1 {G ln (u,v)}}:

[0054] Among them, G ln (u,v)=F ln (u,v)×H(u,v)=F i,ln (u,v)×H(u,v)+F r,ln (u,v)×H(u,v)=G i,ln (u,v)+G r,ln (u,v); filter function

[0055] F ln (u,v)=F[ln i(x,y)+ln r(x,y)]=F i,ln (u,v)+F r,ln (u,v); F -1 () is the inverse Fourier transform function; F i,ln (u,v) is the preset lighting function, F r,ln (u, v) is the preset reflection function, M, N are the preset size parameters of the fingerprint collection image, 1≤u≤M, 1≤v≤N; high frequency gain coefficient γ H >1, low frequency gain coefficient γ L <1, D0≥3 is the cutoff frequency, c is the sharpening constant, γ H ≥c≥γ L ; ln() is a logarithmic function; i(x,y) is the total amount of light incident on the fingerprint collection scene, r(x,y) is the total amount of light reflected by the fingerprint, x is the x-axis coordinate of the pixel, and y is the y-axis coordinate of the pixel;

[0056] Step 3: Calculate the complex direction field of the fingerprint image after pixel optimization processing By using the symmetric filter h=(x+iy)g(x,y), we can calculate R(x,y)=||[(x+iy)g(x,y)]*z(x,y)||, and take the pixel point (x, y) corresponding to the maximum value of R(x,y) as the center point; where f x and f y represents the gradient of the preprocessed fingerprint image in the x and y directions, g(x,y)=exp(-(x 2 +y 2 ) / (2σ 2 )), σ is the preset coefficient value;

[0057] Step 4: Select any pixel point P(x,y), set a 3×3 window centered at P(x,y), define P as the ridge and valley value of the pixel point P(x,y), 1 represents the fingerprint ridge, and 0 represents the fingerprint valley; the remaining windows in the 3×3 window are pixel sequences P0, P1, ... Pi ...P7;

[0058] If the 3×3 window satisfies the logical rule 2≤N(P(x,y))≤6and(T(P)=1)and(P0P2P4=0orT(P)≠0)and(P2P4P6=0orT(P)≠0), then the corresponding pixel point P(x,y) is eliminated, and all the pixels in the image are traversed to obtain the final fingerprint recognition area I(x,y);

[0059] in, T(P) represents the pixel sequence P0, P1, ... P in the 3×3 window of P i ...the number of changes from 0 to 1 in P7, P0P2P4 and P2P4P6 represent the logical products of each other;

[0060] Step 5: Collect fingerprint features of the effective fingerprint recognition image I(x, y), perform touch fingerprint recognition, and output the fingerprint recognition result and center point parameters.

[0061] The existing signal graph collected by the touch screen has the problem that the fingerprint image is not clear, and direct positioning and fingerprint recognition result in high errors. This embodiment uses a pixel optimization processing function to perform pixel optimization processing, and then performs fingerprint recognition and positioning, thereby improving the recognition accuracy of the touch screen.

[0062] Preferably, the interlaced scanning method comprises:

[0063] Step 1.1, define any corner point (x1, y1) of the touch screen grid as the origin, define the search step length as L, start from the origin, search along the x direction, if a point is detected to exceed the threshold, it is defined as a touch point, and the position and value of the point are recorded and numbered in sequence, otherwise continue searching;

[0064] Step 1.2, update the point (x1, y1+N*L) as the origin, and return to step 1.1 until both the x direction and the y direction are searched, completing the preliminary positioning search, where N is an integer;

[0065] Step 1.3, take out the touch points one by one, update the touch points taken out this time as the origin, update the search step size to L / 2, search in sequence along the x direction, do not search the points that have been searched before, and automatically halve the search step size if the search is out of range, and continue searching until the step size is reduced to 1. If a new touch point appears during the search process, it is defined as a new point that needs to be searched in the y direction, and execute step 1.4, otherwise execute step 1.5;

[0066] Step 1.4: The search step length is L / 2 and remains unchanged. Search is performed in sequence along the y direction. The previously searched points are no longer searched. If the search is out of range, the search step length is automatically halved. The search is continued until the step length is reduced to 1. If a new touch point appears during the search process, it is defined as a new point that needs to be searched in the x direction. Step 1.4 is executed. Otherwise, step 1.5 is executed.

[0067] Step 1.5, until there are no new points to be retrieved, the retrieval is terminated, and the retrieved touch points are set as the effective area of ​​the fingerprint image contour map.

[0068] Furthermore, the touch fingerprint recognition includes:

[0069] Step 5.1, draw concentric circles with the center point as the center, divide the fingerprint image into B annular areas, and finally divide each annular area into K sector areas, where K and B are both predefined constants;

[0070] Step 5.2, calculate each sector S sq The sector fingerprint characteristic value V sqθ As Code1;

[0071]

[0072] Among them, F sqθ (x,y) is the sector area S sq The gray value of each pixel, P sqθ Represents the sector area S sq The average gray value of the inner pixels, n sq The annular area S sq The number of inside, 0<sq≤B×K-1,θ={0°,(360° / K),2*(360° / K),3*(360° / K),...≤180°}

[0073] Step 5.3, after rotating the fingerprint image (180° / K), repeat step 8.2 to extract each sector S sq The sector fingerprint characteristic value V sqθ As Code2;

[0074] Step 5.4, rotate Code1 and Code2 by R×(360° / K) (R=0,1,2...K-1) to obtain Code1' and Code2';

[0075] Step 5.5, input Code1 and Code2, Code1' and Code2' in step 5.4 into the historical fingerprint library for matching calculation;

[0076] Step 5.6, output the fingerprint recognition result.

[0077] Furthermore, the touch fingerprint recognition may also include:

[0078] Step A: define the fingerprint feature image to be matched and verified in the historical fingerprint library as the reference image I C , the collected target image to be matched is Define reference image I C And the target image after polar coordinate transformation The related relationships are as follows:

[0079] Among them, α z is the scale offset parameter, is the rotation offset parameter;

[0080] Step B, calculate the reference image I C Radial projection in polar coordinates Target Image Projection in the radial direction K C (i) and Taking the logarithm gives LK C (i) and LK C (i) and The translation difference is used as the scale offset parameter α z ;

[0081]

[0082]

[0083]

[0084] K i =K max The number of samples in the angle direction, ce() represents the smallest integer greater than or equal to the value in the brackets, and fl() represents the largest integer less than or greater than the value in the brackets; the size of the effective fingerprint recognition image I(x,y) is 2K max ×2K max , n r =K max is the number of radial sampling, is the number of sampling in the angular direction;

[0085] Step C, calculate the reference image I according to the scale offset parameter in step B C and the target image Radial and angular projections:

[0086]

[0087]

[0088] right and Perform normalization calculation to calculate the translation of the highest point according to Calculate the rotation offset parameters

[0089] Step D, the rotation offset parameter φ z and scale shift parameter α z Bring in step A to correct the target image, and according to Calculate ∈ z The location point corresponding to the minimum value is the center point of the target image;

[0090] Step D: The target image after image correction When the error rate is less than the preset threshold after comparison with the reference image, the fingerprint recognition is defined as successful.

[0091] This embodiment uses a pixel optimization processing function to complete pixel optimization processing, and then performs fingerprint recognition and positioning, thereby improving the recognition accuracy of the touch screen. The interlaced scanning method changes the current row-by-row and column-by-column scanning, thereby improving efficiency. At the same time, by correcting the offset of the target fingerprint image, the number of fingerprints in the fingerprint library is reduced, and high-efficiency and high-precision fingerprint recognition and touch positioning are completed.

[0092] Although the above describes the illustrative specific embodiments of the present invention so that those skilled in the art can understand the present invention, the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, all inventions and creations using the concepts of the present invention are protected.

Claims

1. A method for operating a high-precision touch control device, characterized in that: The operating method of the high-precision touch control device includes: Step 1: define the touch screen as a grid, scan the touch area using an interlaced scanning method, and collect touch signals; Step 2: Optimize the fingerprint image pixel by pixel optimization function. The pixel optimization function is -g(x, y) = exp{F -1 {G ln (u, v)}}; Among them, G ln (u, v) = F ln (u, v) × H(u, v) = F i，ln (u, v) × H(u, v) + F r，ln (u, v) × H(u, v) = G i，ln (u, v) + G r，ln (u, v); filtering function F ln (u, v) = F [lni (x, y) + lnr (x, y)] = F i，ln (u,v)+F r，ln (u, v); F -1 () is the inverse Fourier transform function; F i，ln (u, v) is the preset lighting function, F r，ln (u, v) is the preset reflection function, M, N are the preset size parameters of the fingerprint collection image, 1≤u≤M, 1≤v≤N; high frequency gain coefficient γ H >1, low frequency gain coefficient γ L <1, D0≥3 is the cutoff frequency, c is the sharpening constant, γ H ≥c≥γ L ; ln() is a logarithmic function; i(x, y) is the total amount of light incident on the fingerprint collection scene, r(x, y) is the total amount of light reflected by the fingerprint, x is the x-axis coordinate of the pixel, and y is the y-axis coordinate of the pixel; Step 3: Calculate the complex direction field of the fingerprint image after pixel optimization processing By using the symmetric filter h = (x + iy) g (x, y), R (x, y) = || [(x + iy) g (x, y)] * z (x, y) || is calculated, and the pixel point (x, y) corresponding to the maximum value of R (x, y) is taken as the center point; where f x and f y represents the gradient of the preprocessed fingerprint image in the x and y directions, g(x, y) = exp(-(x 2 +y 2 ) / (2σ 2 )), σ is the preset coefficient value; Step 4: Select any pixel point P(x, y), set a 3×3 window centered at P(x, y), define P as the ridge and valley value of the pixel point P(x, y), 1 represents the fingerprint ridge, and 0 represents the fingerprint valley; the remaining windows in the 3×3 window are pixel sequences P0, P1, ...P i ...P7; If the 3×3 window satisfies the logical rule 2≤N(P(x, y))≤6and(T(P)=1)and(P0P2P4=0orT(P)≠0)and(P2P4P6=0orT(P)≠0), then the corresponding pixel point P(x, y) is eliminated, and all the pixels in the image are traversed to obtain the final fingerprint recognition area I(x, y); in, T(P) represents the pixel sequence P0, P1, ...P in the 3×3 window of P i ...the number of changes from 0 to 1 in P7, P0P2P4 and P2P4P6 represent their respective logical products; Step 5: Collect fingerprint features of the effective fingerprint recognition image I(x, y), perform touch fingerprint recognition, and output the fingerprint recognition result and center point parameters; Wherein, scanning the touch area by using the interlaced scanning method includes: Step 1.1, define any corner point (x1, y1) of the touch screen grid as the origin, define the search step length as L, start from the origin, and search along the x direction. If a point is detected to exceed the threshold, it is defined as a touch point, and the position and value of the point are recorded and numbered in sequence, otherwise continue searching; Step 1.2, update the point (x1, y1+N*L) as the origin, and return to execute step 1.1 until the search in both the x direction and the y direction is completed, completing the preliminary positioning search, where N is an integer; Step 1.3, take out the touch points one by one, update the touch points taken out this time as the origin, update the search step size to L / 2, search in sequence along the x direction, do not search the points that have been searched before, and automatically halve the search step size if the search is out of range, and continue searching until the step size is reduced to 1. If a new touch point appears during the search process, it is defined as a new point that needs to be searched in the y direction, and execute step 1.4, otherwise execute step 1.5; Step 1.4: The search step length is L / 2 and remains unchanged. Search is performed in sequence along the y direction. The previously searched points are no longer searched. If the search is out of range, the search step length is automatically halved. The search is continued until the step length is reduced to 1. If a new touch point appears during the search process, it is defined as a new point that needs to be searched in the x direction. Step 1.3 is executed. Otherwise, step 1.5 is executed. Step 1.5, until there are no new points to be retrieved, the retrieval is terminated, and the retrieved touch points are set as the effective area of ​​the fingerprint image contour map.

2. The operating method of the high-precision touch control device according to claim 1, characterized in that: The touch fingerprint recognition comprises: Step 5.1, draw concentric circles with the center point as the center, divide the fingerprint image into B annular areas, and finally divide each annular area into K sector areas, where K and B are both predefined constants; Step 5.2, calculate each sector S sq The sector fingerprint characteristic value V sqθ As Code1; Among them, F sqθ (x, y) is the fan-shaped area S sq The gray value of each pixel, P sqθ Represents the sector area S sq The average gray value of the inner pixels, n sq S in the annular region sq The number of, 0<sq≤B×K-1, θ={0°,(360° / K),2*(360° / K),3*(360° / K),...≤180°}; Step 5.3, rotate the fingerprint image Then, repeat step 5.2 to extract each sector S sq The sector fingerprint characteristic value V sqθ As Code2; Step 5.4, rotate Code1 and Code2 separately R=0,1,2...K-1,get Code1' and Code2'; Step 5.5, input Code1 and Code2, Code1' and Code2' in step 5.4 into the historical fingerprint library for matching calculation; Step 5.6, output the fingerprint recognition result.

3. The operating method of the high-precision touch control device according to claim 1, characterized in that: The touch fingerprint recognition may also include: Step A: define the fingerprint feature image to be matched and verified in the historical fingerprint library as the reference image I C , the collected target image to be matched is Define reference image I C And the target image after polar coordinate transformation The related relationships are as follows: Among them, α Z is the scale shift parameter, φ Z is the rotation offset parameter; Step B, calculate the reference image I C Radial projection in polar coordinates Target image Projection in the radial direction K C (i) and Taking the logarithm gives LK C (i) and LK C (i) and The translation difference is used as the scale offset parameter α Z ; K i =K max The number of samples in the angle direction, ce() represents the smallest integer greater than or equal to the value in the brackets, and fl() represents the largest integer less than or greater than the value in the brackets; the size of the effective fingerprint recognition image I (x, y) is 2K max ×2K max , n r =K max is the number of radial sampling, n φ =8K i is the number of sampling in the angular direction; Step C, calculate the reference image I according to the scale offset parameter in step B C and the target image Radial and angular projections: right and Perform normalization calculation to calculate the translation of the highest point according to Calculate the rotation offset parameters Step D, rotate the offset parameters and scale shift parameter α Z Bring in step A to correct the target image, and according to Calculate ∈ Z The location point corresponding to the minimum value is the center point of the target image; Step D: The target image after image correction When the error rate is less than the preset threshold after comparison with the reference image, the fingerprint recognition is defined as successful.

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