A palm vein image recognition and matching method using valley features and a system thereof

Abstract

The application discloses a palm vein image recognition and matching method and system using valley-shaped features, and the method comprises the following steps: S1, photographing a user's palm, processing collected palm vein information to obtain palm vein features, and uploading the palm vein features to a database; mapping the gray value of each point G(j, i) in the gray image in step S1 in the matrix G1 according to the same row and column coordinates (j, i), so that the value of G1(j, i) is equal to the gray value of the point G(j, i); S3, performing curve fitting processing on the extracted column vector; S4, finding the lowest point, i.e. a feature point, in the polynomial Y N obtained in step S3, and recording the obtained feature point set as P c ; S5, repeating the above steps S2 and S3 to traverse the entire image G1, so as to extract all feature points. Compared with the prior art, the image enhancement process is simpler, the image can be quickly processed, and the user can be quickly recognized in a big data scene.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of image recognition, and particularly relates to a palm vein image recognition and matching method using valley shape features and a system thereof. BACKGROUND

[0002] In the prior art, the processing of palm veins as a biological feature is generally divided into two categories:

[0003] The first is a deep learning method, which has the advantages of high matching accuracy, but needs a large amount of image preprocessing work and requires a large amount of computing power;

[0004] The second is a traditional method represented by hash coding, which has the advantages of high speed and low computing power requirement, but has the disadvantage of low matching accuracy. SUMMARY

[0005] In order to solve the above technical problems existing in the prior art, the present application provides a palm vein image recognition and matching method using valley shape features and a system thereof, which skips the image preprocessing link that consumes the most computing power in the deep learning method, and optimizes the matching accuracy of the hash method through the processing of feature points. The technical scheme adopted by the present application is:

[0006] A palm vein image recognition and matching method using valley shape features, characterized in that it specifically comprises the following steps:

[0007] A palm vein image recognition and matching method using valley shape features, characterized in that it specifically comprises the following steps:

[0008] S1: Taking a photo of the user's palm, and recording the gray image of the image target area as HG, and processing the collected palm vein information to obtain palm vein features and uploading to the database:

[0009] The palm vein information is taken as an image matrix with a size of M*N, and is recorded as matrix G, where m is the number of rows of matrix G, the sequence number of a row in matrix G is j, the number of columns of matrix G is n, the sequence number of a column in matrix G is i, the element of the column with the sequence number i in matrix G is recorded as G(i, i), the element of the row with the sequence number j in matrix G is recorded as G(j, ), the element of the row with the sequence number j and the column with the sequence number i in matrix G is recorded as G(j, i), i∈[1, n], j∈[1, m]; an image matrix with the same size as the preprocessed image, that is, m*n, is set, and is recorded as matrix G1, where m is the number of rows of matrix G1, n is the number of columns of matrix G1, the sequence number of a row in matrix G1 is also j, the sequence number of a column in matrix G1 is i, the element of the row with the sequence number i in matrix G1 is recorded as G1(i, i), the element of the row with the sequence number j in matrix G1 is recorded as G1(j, ), and the element of the row with the sequence number j and the column with the sequence number i in matrix G1 is recorded as G1(j, i);

[0010] S2: The numerical calculation of each point G1(j, i) is as follows:

[0011] The gray value of each point HG(j, i) in the gray image in step S1 is mapped in matrix G1 according to the same row and column coordinates (j, i), so that the value of G1(j, i) is equal to the gray value of the point HG(j, i); the point G1(j, i) is represented as g in the matrix. ji , which has a matrix expression as follows:

[0012]

[0013] S3, extract the column vector in matrix G1, and record the obtained one-dimensional discrete vector as 1*N, where N is equal to the value of j; the extracted column vector is subjected to curve fitting processing; according to Taylor's formula, any function can be decomposed into a polynomial expression that approximates the function, and by taking all points in the column vector, curve fitting is performed by the least square method, the fitting order is 18, and the fitted polynomial is recorded as Y N , then:

[0014]

[0015] S4, for the fitted polynomial Y N in step S3, find its local minimum point, and the obtained minimum point is the feature point P, and the obtained feature point set is recorded as P c ; the calculation method is:

[0016] Let there be a vector L i (x, g), where the data of each point in it comes from the polynomial Y N ; for each data point in the vector L i (x, g), there is an error sum of squares S, and the vector Li (x, g) is expressed in matrix form as follows:

[0017]

[0018]

[0019] The sum of squared errors S is expressed as:

[0020] S=(xA-g) T (xA-g) (5)

[0021] Where x is a Vandermonde matrix and A is the polynomial a i The coefficient vector of coefficients, g is the polynomial Y N Output vector in matrix G1;

[0022] For the optimal function, it should satisfy:

[0023]

[0024] Then the solution formula for the polynomial coefficient set A is:

[0025] A=(x T x) -1 x T g (7);

[0026] S5. Repeat steps S2 and S3 above to traverse the entire image G1, thereby extracting all feature points.

[0027] Furthermore, in step S1, the method for collecting palm vein information is as follows: an infrared sensor collects the user's palm vein information, uses infrared light with a wavelength of 850nm to irradiate the user's palm, collects the palm vein pattern reflected by the palm, and grayscales and binarizes the palm vein pattern to obtain an image matrix of uniform size as the palm vein information.

[0028] Furthermore, in step S1 , the size of the grayscale image G is M*N, where M=N=128.

[0029] Furthermore, in step S4, the fitted polynomial Y N Solve its second-order derivative f″(P x ), when f″(P x )=0, record the horizontal coordinate of the point as P x , and calculate the point P respectively x+1 and point P x-1 The first-order derivative f′(P x-1 ) and f′(P x+1 ), when f′(P x+1 )>0 and f′(Px-1 )<0, record the point P x is the feature point, and the obtained feature point set is recorded as P c .

[0030] Furthermore, in step S4, the feature point set P c The grayscale value of the pixel is reassigned. Since the grayscale value of the pixel is an integer in the range of 0-255, and the smaller the value, the darker the point is in the human eye, its grayscale value is reassigned to 0, and a new sub-image G2 is generated, and the hash code H of the new sub-image G2 is obtained.

[0031] Furthermore, in step S5, the new sub-image G2 is traversed using a sliding window W of size 8*8. The traversal rule of the window W is: the pixels obtained in each traversal are not repeated, and their hash codes H are obtained. l , and uploaded to the database, all hash codes in the database constitute the set H t ; By comparing the user's database, the system will get the hash code H l , with the H t All hash codes H in the are compared, thereby achieving rapid extraction, rapid identification and search of palm vein features.

[0032] Furthermore, in step S4, the hash coding is performed as follows:

[0033] S41. Divide G2 into 256 8*8 sub-matrices with no intersection between them, denoted as z1~z 256 , calculate the average value of each matrix, recorded as Equ1 n , Equ1 n =∑z n ÷64, where Map to the new matrix G3;

[0034] S42. In the new matrix G3, divide it into 64 4*4 sub-matrices, denoted as zz1~zz 64 , take the average value of each matrix, recorded as Equ2 n2 , equ2 n2 =∑zz n2 ÷16

[0035] in Mapped into the new matrix G4;

[0036] S43. Calculate the average value of G4, recorded as Equ3, Equ3 = ∑G4 ÷ 64; then compare each element in the matrix with the average value, record the value greater than the average value as 1, and record the value less than the average value as 0, and generate a 64-bit hash code H l ;

[0037] S44, the hash code H obtained in step S43 l , calculate the Hamming distance Hamdist with each code in the database one by one, and set the success condition. If the condition is not met, it will be regarded as a failure. Set H t A total of t data are stored, where each hash code is recorded in the order of entry as H1, H2, ..., H t ; then:

[0038]

[0039] Among them, the matching success condition is set to Hamdist≤5.

[0040] Furthermore, a system supporting the above-described method is characterized in that it includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method by running or executing the computer program and / or module stored in the memory, and calling data stored in the memory to execute the computer program; the memory is used to store the computer program and / or module; and the processor executes the computer program in the following system units:

[0041] An information collection unit, used to collect palm vein information;

[0042] The data processing unit is used to process the collected palm vein information to obtain palm vein features and encode and upload them to the database;

[0043] The hash code calculation unit is used to encode each palm vein feature in the database and calculate the similarity between each palm vein feature code.

[0044] Furthermore, the processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete component gate circuit or a transistor logic device, or a discrete hardware component; the general-purpose processor may be a microprocessor.

[0045] Furthermore, the memory includes a program storage area and a data storage area, wherein the program storage area can store an operating system and applications required for at least one function; the data storage area stores data created according to the use of the mobile phone; the memory can include a high-speed random access memory or a non-volatile memory.

[0046] Compared with the prior art, the beneficial effects of the present invention are embodied in:

[0047] 1. The palm vein information collected is processed, the processed palm vein image is subjected to hash coding, the code is uploaded to a database, each code in the database is calculated, the similarity between each palm vein code is confirmed by calculating the Hamming distance, and the threshold is set, thereby realizing the user recognition function.

[0048] 2. The image preprocessing step is simplified by highlighting feature points, and the subsequent hash coding has higher security, while ensuring matching speed and improving matching accuracy.

[0049] 3. The image preprocessing link in the deep learning method is skipped, and the matching accuracy of the hash method is optimized by processing the feature points.

[0050] 4. The application can run in desktop computers, notebooks, palmtop computers and cloud data centers, and the executable system can include, but is not limited to, processors, memories, server clusters. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The flow chart of the palm vein image feature extraction and matching system using the valley feature is shown.

[0052] Figure 2 The effect of the palm vein image feature extraction and matching system method using the valley feature is shown. Figure 1 .

[0053] Figure 3 The effect of the palm vein image feature extraction and matching system method using the valley feature is shown. Figure 2 . DETAILED DESCRIPTION

[0054] The specific implementation of the embodiments of the application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiments of the application, and is not used to limit the embodiments of the application.

[0055] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0056] The application will be described in detail below with reference to the accompanying drawings and in combination with exemplary embodiments.

[0057] Reference Figure 1 , Figure 2 and Figure 3The application discloses a palm vein image recognition and matching method using a valley-shaped feature, and specifically comprises the following steps.

[0058] S1: photographing a user's palm, and recording a gray-scale image of a target region of the image as HG; palm vein information collected is processed to obtain palm vein features and uploaded to a database.

[0059] The palm vein information is taken as an image matrix with a size of M*N and recorded as matrix G, wherein m is the number of rows of the matrix G, the serial number of a row in the matrix G is j, the number of columns of the matrix G is n, the serial number of a column in the matrix G is i, the element of the column with the serial number i in the matrix G is recorded as G(i,j), the element of the row with the serial number j in the matrix G is recorded as G(j,i), the element of the row with the serial number j and the column with the serial number i in the matrix G is recorded as G(j,i), i∈[1,n] and j∈[1,m]; an image matrix with the same size as the preprocessed image and recorded as matrix G1 is set, wherein m is the number of rows of the matrix G1, n is the number of columns of the matrix G1, the serial number of a row in the matrix G1 is j, the serial number of a column in the matrix G1 is i, the element of the row with the serial number i in the matrix G1 is recorded as G1(i,j), the element of the row with the serial number j in the matrix G1 is recorded as G1(j,i), and the element of the row with the serial number j and the column with the serial number i in the matrix G1 is recorded as G1(j,i).

[0060] S2: the numerical calculation of each point G1(j,i) is as follows:

[0061] The gray-scale value of each point HG(j,i) in the gray-scale image in step S1 is mapped in the matrix G1 according to the same row and column coordinates (j,i), so that the value of G1(j,i) is equal to the gray-scale value of the point HG(j,i); and the point G1(j,i) is expressed as g in the matrix. ji The matrix is expressed as follows:

[0062]

[0063] S3: a column vector in the matrix G1 is extracted, and a one-dimensional discrete vector obtained is recorded as 1*N, wherein N is equal to the value of j; curve fitting processing is performed on the extracted column vector; according to the Taylor formula, any function can be split into a polynomial expression approximating the function; through the least square method, all points in the column vector are taken to perform curve fitting, the fitting order is 18, and the fitting polynomial is recorded as Y N Then,

[0064]

[0065] S4: the fitting polynomial Y N, find its local minimum point, and the minimum point is the feature point P, and the obtained feature point set is denoted as P c ; the calculation method is as follows:

[0066] Suppose that there is a vector L i (x, g), wherein each point data is from the polynomial Y N , and there is an error square sum S for each data point in the vector L i (x, g). The vector L i (x, g) is expressed in a matrix form as follows:

[0067]

[0068]

[0069] The error square sum S is expressed as:

[0070] S = (xA-g) T (xA-g) (5)

[0071] wherein x is a Vandermonde matrix, A is a coefficient vector composed of polynomial a i coefficients, and g is a polynomial Y N in the matrix G1.

[0072] For the optimal function, the following should be satisfied:

[0073]

[0074] The solving formula of the polynomial coefficient set A is obtained as follows:

[0075] A = (x T x) -1 x T g (7);

[0076] S5, repeat the steps S2 and S3, and traverse the entire image G1, so as to extract all feature points.

[0077] In an embodiment, in the step S1, the method for collecting the palm vein information is as follows: an infrared sensor collects palm vein information of a user, an infrared ray with a wavelength of 850 nm is used to irradiate a palm of the user, a palm vein pattern reflected by the palm is collected, and a size-uniform image matrix is obtained by performing grayscale and binaryzation processing on the palm vein pattern, so as to obtain the palm vein information.

[0078] In an embodiment, in the step S1, the size of the gray-scale image HG is M*N, wherein M=N=128.

[0079] In an embodiment, in the step S1, the reference example of the gray-scale image HG is as followsFigure 1 .

[0080] In an embodiment, in step S4, the fitted polynomial Y N is solved for its second derivative f″(P x ), when f″(P x )=0, the abscissa of the point is recorded as P x , and the first derivatives f′(P x+1 ) and f′(P x-1 ) of the points P x-1 and P x+1 are calculated respectively, when f′(P x+1 )>0 and f′(P x-1 )<0, the point P x is recorded as a feature point, and the obtained feature point set is recorded as P c .

[0081] In an embodiment, in step S4, the gray value of the feature point set P c is revalued, since the gray value of the pixel point is an integer in the interval of 0-255, and the smaller the value is, the darker the point is in the human eye, so the gray value is revalued as 0, and a new sub-image G2 is generated, and the hash code H of the new sub-image G2 is obtained.

[0082] In an embodiment, in step S4, for the newly generated sub-image G2, the effect Figure 2 .

[0083] In an embodiment, in step S5, the new sub-image G2 is traversed using a sliding window W of size 8*8, and the traversal rule of the window W is that the pixel points obtained each time are not repeated, the hash code H l is obtained and uploaded to the database, all the hash codes in the database constitute a set H t ; by comparing the hash code H l of the user this time with all the hash codes H in the set H t , the fast extraction, fast recognition and fast search of the palm vein features are realized.

[0084] In an embodiment, in step S4, the hash coding method is as follows:

[0085] S41, divide G2 into 256 sub-matrices of 8*8 that do not intersect each other, recorded as z1-z 256 , calculate the average value of each matrix, recorded as equ1 n , equ1 n =∑z n ÷64, wherein Mapping to a new matrix G3;

[0086] S42, in the new matrix G3, and then divide it into 4*4 sub-matrix, a total of 64, recorded as zz1~zz 64 , the average value of each matrix is equ2 n2 , equ2 n2 =∑zz n2 ÷16

[0087] Wherein Mapping to a new matrix G4;

[0088] S43, calculate the average value of G4, recorded as equ3, equ3=∑G4÷64; then the elements in the matrix are compared with the average value respectively, greater than the average value is recorded as 1, less than the average value is recorded as 0, and 64-bit hash code H l is generated;

[0089] S44, the hash code H l obtained in step S43 is calculated with each code in the database to calculate the Hamming distance Hamdist, and the success condition is set, and those that do not meet the condition are regarded as failure; the set H t stores t data, wherein each hash code is recorded as H1, H2,..., H t ; then:

[0090]

[0091] Wherein, the matching success condition is set as Hamdist≤5.

[0092] In one embodiment, the method described in the application is matched with a system, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor realizes the method by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory to execute the computer program; the memory is used to store the computer program and / or modules; the processor executes the computer program in the following system units:

[0093] An information acquisition unit is used to acquire palm vein information;

[0094] A data processing unit is used to process the acquired palm vein information to obtain palm vein features and encode and upload to the database;

[0095] A hash code calculation unit is used to encode each palm vein feature in the database and calculate the similarity between each palm vein feature code.

[0096] In an embodiment, the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the fast palm vein feature extraction system, and is connected with various sub-regions of the fast palm vein feature extraction system through various interfaces and lines.

[0097] In an embodiment, the memory includes a program storage area and a data storage area. The program storage area can store an operating system and application programs required by at least one function (such as a sound playing function, an image playing function, etc.). The data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory such as a hard disk, a memory card, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0098] The method of the present application eliminates the image preprocessing step, optimizes the matching accuracy of the hash method through the processing of the feature points, and achieves the beneficial effects of feature extraction and recognition with low calculation cost.

[0099] Although the embodiments of the present application have been shown and described above, it should be understood that the above-described embodiments are exemplary, and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.

Claims

1. A method for palm vein image recognition and matching using valley features, characterized by, Specifically comprising the following steps: S1: photographing the palm of the user, and recording the gray image of the image target region as HG, and the method for processing the collected palm vein information to obtain palm vein features and uploading to the database is: The palm vein information is taken as an image matrix with a size of m*n, and is recorded as matrix G, wherein m is the number of rows of matrix G, the serial number of the row in matrix G is j, the number of columns of matrix G is n, the serial number of the column of matrix G is i, the element of the column with serial number i in matrix G is recorded as G(i, i), the element of the row with serial number j in matrix G is recorded as G(j, ), the element of the row with serial number j and the column with serial number i in matrix G is recorded as G(j, i), i∈[1, n], j∈[1, m]; an image matrix with the same size as the preprocessed image, m*n, is set, and is recorded as matrix G1, wherein m is the number of rows of matrix G1, n is the number of columns of matrix G1, the serial number of the row in matrix G1 is also j, the serial number of the column is i, the element of the row with serial number i in matrix G1 is recorded as G1(i, i), the element of the row with serial number j in matrix G1 is recorded as G1(j, ), and the element of the row with serial number j and the column with serial number i in matrix G1 is recorded as G1(j, i); The numerical calculation method of each point G1(j, i) is as follows: The gray scale value of each point HG(j,i) in the gray scale image in step S1 is mapped in matrix G1 according to the same row and column coordinates (j,i) so that the value of G1(j,i) is equal to the gray scale value of the point HG(j,i); the point G1(j,i) is represented as g in the matrix ji , which can be expressed as follows: S3, extract column vectors in the matrix G1, and denote the obtained one-dimensional discrete vector as 1*N, where N is equal to the value of j; perform curve fitting processing on the extracted column vectors; according to the Taylor formula, any function can be split into a polynomial expression that approximates the function, and through the least square method, take all points in the column vector to perform curve fitting, with a fitting order of 18, and denote the fitted polynomial as Y N Then, S4、to the step S3 in the fitted polynomial Y N , find its local minimum point, the minimum point obtained is the feature point P, the feature point set obtained is recorded as P c ; the calculation method is: Let there be a vector L i (x,g), where each point data comes from a polynomial Y N , for each data point in the vector L i (x,g), there is a sum of square errors S, the vector Li(x,g) is expressed in matrix form as follows: The error sum of squares S is represented as: S = (xA- g) T (xA- g) (5) where x is a Vandermonde matrix, A is a polynomial a i a coefficient vector consisting of coefficients of the polynomial Y N an output vector in the matrix G1; For the optimal function, the following should be satisfied: Then the solving formula of the polynomial coefficient set A is obtained as: A = (x T x) -1 x T g (7); In step S4, the fitted polynomial Y N is solved for its second derivative f″(P x ), and when f″(P x ) = 0, the abscissa of the point is recorded as P x , and the first derivatives f′(P x+1 ) and f′(P x-1 ) of the points P x-1 and P x+1 are calculated, respectively. When f′(P x+1 ) > 0 and f′(P x-1 ) < 0, the point P x is recorded as a feature point, and the obtained set of feature points is recorded as P c ; In step S4, the hash coding mode is as follows: S41, divide g2 into 256 8*8 sub-matrices without intersection, noted as z1~z256 256 , calculate the average value of each matrix, noted as equ1 n , equ1 n =∑z n ÷64, wherein map to new matrix G3; S42、In the new matrix G3, again divide it into 4*4 sub-matrices totally 64, recorded as zz1~zz64 64 , take the average value of each matrix, recorded as equ2 n2 , equ2 n2 =∑zzn2÷16 wherein are mapped into the new matrix G4; S43, calculate the average value of G4, recorded as equ3, equ3 =∑G4 ÷ 64; then compare each element in the matrix with the average value respectively, greater than the average value is recorded as 1, less than the average value is recorded as 0, to generate a 64-bit hash code H l ; S44, the hash code H obtained in step S43 is compared with the hash code H of the data in the database l The Hamming distance Hamdist is calculated one by one with each piece of encoding in the database, and a success condition is set, and those not meeting the condition are regarded as failure; the set H t A total of t pieces of data are stored, wherein each piece of hash code is recorded in the order of entry as H1, H2,..., H t ; then, Hamdist = H l ⊕H t (8) Wherein, the matching success condition is set as Hamdist≤5; S5, repeat the above steps S2 and S3 to traverse the entire image G1, so as to extract all feature points. In step S5, a sliding window W of 8*8 size is used to traverse the new sub-image G2, and the traversal rule of the window W is that the pixel points obtained each time are not repeated, and the hash code H thereof is obtained l , and uploaded to the database, and all the hash codes in the database constitute a set H t ; by comparing the hash code H l of the user this time in the database, the system obtains the hash code H of this time t , and compares all the hash codes H in the H t , so as to realize the rapid extraction, rapid identification and rapid search of the palm vein features.

2. The method of palm vein image recognition and matching using valley features as claimed in claim 1, wherein, In step S1, the method for collecting palm vein information is: an infrared sensor collects the palm vein information of the user, an infrared ray with a wavelength of 850nm is used to irradiate the palm of the user, the palm reflection pattern collected is processed by graying and binarization to obtain an image matrix with a uniform size as the palm vein information.

3. The method of claim 1, wherein the palm vein image recognition and matching using the valley-shaped features is characterized by, In step S1, the size of the gray image G is M*N, wherein M=N=128.

4. The method of claim 1, wherein the palm vein image recognition and matching using the valley-shaped features is characterized by, In step S4, the gray values of the feature point set P c are re-assigned. Since the gray value of a pixel is an integer in the interval 0-255, and the smaller the value, the darker the point in the human eye, the gray value is re-assigned to 0, and a new sub-image G2 is generated, and the hash code H of the new sub-image G2 is obtained.

5. A system for use with the method according to any one of claims 1 to 4, characterized in that The processor, the memory, and the computer program stored in the memory and executable on the processor are included, the processor implements the method by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory to execute the computer program; the memory is used to store the computer program and / or modules; the processor executes the computer program to run in the following system units: An information collection unit is used to collect palm vein information; A data processing unit is used to process the collected palm vein information to obtain palm vein features and encode and upload to the database; A hash coding calculation unit is used to encode each palm vein feature in the database and calculate the similarity between each palm vein feature code.

6. The system of claim 5, wherein, The processor can be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete component gate circuit or transistor logic, a discrete hardware component; the general purpose processor can be a microprocessor.

7. The system of claim 6, wherein, The memory includes a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function; the data storage area stores data created according to the use of the mobile phone; the memory can include a high-speed random access memory or a non-volatile memory.

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