A method and device for user identity recognition of dental point cloud data

Abstract

The present invention discloses a method and device for user identity recognition of dental point cloud data. The method includes: obtaining a dental point cloud data set and a user's standard dental point cloud data set; the dental point cloud data set includes dental point cloud data; the user's standard dental point cloud data set includes a plurality of dental point cloud data pre-collected by the user; preprocessing the dental point cloud data set to obtain a preprocessed data set; performing recognition processing on the preprocessed data set and the user's standard dental point cloud data set to obtain user recognition information; the user recognition information is used to represent the user identity information corresponding to the dental point cloud data set. The method of the present invention effectively realizes the accurate extraction of user dental features by establishing a first distance calculation model and a second distance calculation model, and realizes the comprehensive analysis of user identity features through the fusion weighted processing, improving the accuracy and efficiency of user identity recognition.

Description

Technical Field

[0001] The present invention relates to the fields of identity recognition and medical information processing, and particularly to a method and device for user identity recognition based on dental point cloud data. Background Art

[0002] Currently, for user identity recognition methods, there are mainly DNA recognition, fingerprint recognition, face recognition, etc. Although the DNA recognition method has high accuracy, the technical implementation process is cumbersome and time-consuming, and it is not suitable for large-scale matching. Moreover, information such as fingerprints and faces is easily damaged. Teeth are the hardest and most indestructible parts of the human body, so they are considered the best choice for identity recognition.

[0003] How to achieve fast and accurate user identity recognition based on dental data is an urgent problem to be solved currently. Summary of the Invention

[0004] Aiming at the problem of how to achieve fast and accurate user identity recognition based on dental data, the present invention discloses a method and device for user identity recognition based on dental point cloud data.

[0005] In the first aspect of the embodiments of the present application, a method for user identity recognition based on dental point cloud data is disclosed, including:

[0006] S1, obtaining a dental point cloud data set and a user dental standard point cloud data set; the dental point cloud data set includes dental point cloud data; the user dental standard point cloud data set includes a plurality of dental point cloud data pre-collected by a user;

[0007] S2, preprocessing the dental point cloud data set to obtain a preprocessed data set;

[0008] S3, performing recognition processing on the preprocessed data set and the user dental standard point cloud data set to obtain user recognition information; the user recognition information is used to represent the user identity information corresponding to the dental point cloud data set.

[0009] The preprocessing of the dental point cloud data set to obtain a preprocessed data set includes:

[0010] S21, performing noise reduction processing on the dental point cloud data set to obtain a noise-reduced data set;

[0011] S22, performing anomaly detection processing on the noise-reduced data set to obtain a first data set;

[0012] S23, performing point cloud registration processing on the first data set to obtain a preprocessed data set.

[0013] The performing anomaly detection processing on the noise-reduced data set to obtain a first data set includes:

[0014] S221. For the data of each type of data attribute in the noise reduction data set, using the data acquisition information of the data as the independent variable and the data value of the data as the dependent variable, perform autoregressive-moving average modeling to respectively obtain the regression models of the data attribute types.

[0015] S222. Use the regression model to perform calculation processing on the independent variable to obtain the regression data value; determine whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than the set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the noise reduction data set, and if it is less than or equal to the first regression discrimination threshold, do not process the data.

[0016] S223. Perform fusion processing on all the data in the noise reduction data set after executing S221 to S222 to obtain the first data set.

[0017] The recognition processing of the preprocessing data set and the user's tooth standard point cloud data set to obtain user recognition information includes:

[0018] S31. Represent the preprocessing data set as a vector to be recognized; the elements of the vector to be recognized are tooth point cloud data.

[0019] S32. Represent the user's tooth standard point cloud data set as a standard point cloud matrix; the row vectors of the standard point cloud matrix are the tooth point cloud data pre-collected for each user; the column dimension of the standard point cloud matrix is the same as the dimension of the vector to be recognized; the row dimension of the standard point cloud matrix is the number of users.

[0020] S33. Perform recognition calculation processing on the vector to be recognized and the standard point cloud matrix to obtain user recognition information.

[0021] The recognition calculation processing of the vector to be recognized and the standard point cloud matrix to obtain user recognition information includes:

[0022] Perform the first distance calculation processing on the vector to be recognized and the standard point cloud matrix to obtain the first distance vector.

[0023] Perform the second distance calculation processing on the vector to be recognized and the standard point cloud matrix to obtain the second distance vector.

[0024] Perform fusion weighting processing on the first distance vector and the second distance vector to obtain the difference vector.

[0025] Determine that the user number corresponding to the element number with the smallest value in the difference vector is the user recognition information.

[0026] The expression for the first distance calculation process is as follows:

[0027]

[0028] where a j is the j-th element of the vector to be recognized, A kj is the element at the k-th row and j-th column of the standard point cloud matrix, ξ is a preset adjustment factor, c k represents the k-th element of the first distance vector, and N is the dimension of the vector to be recognized;

[0029] The expression for the second distance calculation process is as follows:

[0030]

[0031] where μ1 and μ2 are preset weighting coefficients, and d k represents the k-th element of the second distance vector.

[0032] For the fusion weighting process, its calculation expression is:

[0033] e k = λ k c k + ν k / d k + η k | c k - d k | ,

[0034] where e k is the k-th element of the difference vector, λ k is the k-th element of the preset first weighting vector, ν k is the k-th element of the preset second weighting vector, η k is the k-th element of the preset third weighting vector, and M is the row dimension of the standard point cloud matrix.

[0035] In the second aspect of the embodiments of the present application, a user identity recognition device for dental point cloud data is disclosed. The device includes:

[0036] A memory storing executable program code;

[0037] A processor coupled to the memory;

[0038] The processor calls the executable program code stored in the memory to execute the user identity recognition method for dental point cloud data.

[0039] In the third aspect of the embodiments of the present application, a computer-readable storage medium is disclosed. The computer storage medium stores computer instructions, which when called, are used to execute the user identity recognition method for dental point cloud data.

[0040] In the fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed. The information data processing terminal is used to implement the user identity recognition method for dental point cloud data.

[0041] The beneficial effects of the present invention are as follows:

[0042] By establishing a first distance calculation model and a second distance calculation model, the method of the present invention effectively realizes the accurate extraction of user dental features. Through the fusion and weighted processing, the comprehensive analysis of user identity features is realized, improving the accuracy and efficiency of user identity recognition.

[0043] By performing noise reduction and anomaly detection processing on the collected dental data, the present invention effectively suppresses interference and noise, and effectively improves the accuracy of user identity recognition. Description of the Drawings

[0044] Figure 1 It is a flowchart of the implementation of the method of the present invention. Detailed Embodiments

[0045] To better understand the content of the present invention, an embodiment is given here.

[0046] Figure 1 It is a flowchart of the implementation of the method of the present invention.

[0047] In the first aspect of the embodiments of the present application, a user identity recognition method for dental point cloud data is disclosed, including:

[0048] S1. Obtain a dental point cloud data set and a user dental standard point cloud data set; the dental point cloud data set includes dental point cloud data; the user dental standard point cloud data set includes a plurality of dental point cloud data pre-collected by the user;

[0049] S2. Preprocess the dental point cloud data set to obtain a preprocessed data set;

[0050] S3. Perform recognition processing on the preprocessed data set and the user dental standard point cloud data set to obtain user recognition information; the user recognition information is used to represent the user identity information corresponding to the dental point cloud data set;

[0051] The preprocessing of the dental point cloud data set to obtain a preprocessed data set includes:

[0052] S21, perform noise reduction processing on the tooth point cloud data set to obtain a noise-reduced data set;

[0053] S22, perform anomaly detection processing on the noise-reduced data set to obtain a first data set;

[0054] S23, perform point cloud registration processing on the first data set to obtain a preprocessed data set;

[0055] The performing anomaly detection processing on the noise-reduced data set to obtain a first data set includes:

[0056] S221, for the data of each type of data attribute in the noise-reduced data set, use the data acquisition information of the data as the independent variable and the data value of the data as the dependent variable to perform autoregressive-moving average modeling, and respectively obtain the regression models of the type of data attribute;

[0057] S222, use the regression model to perform calculation processing on the independent variable to obtain a regression data value; determine whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, delete the data from the noise-reduced data set, and if it is less than or equal to the first regression discrimination threshold, do not process the data;

[0058] S223, perform fusion processing on all the data in the noise-reduced data set after performing S221 to S222 to obtain a first data set;

[0059] The performing recognition processing on the preprocessed data set and the user's standard tooth point cloud data set to obtain user recognition information includes:

[0060] S31, represent the preprocessed data set as a vector to be recognized; the elements of the vector to be recognized are tooth point cloud data;

[0061] S32, represent the user's standard tooth point cloud data set as a standard point cloud matrix; the row vectors of the standard point cloud matrix are the tooth point cloud data pre-collected for each user; the column dimension of the standard point cloud matrix is the same as the dimension of the vector to be recognized; the row dimension of the standard point cloud matrix is the number of users;

[0062] S33, perform recognition calculation processing on the vector to be recognized and the standard point cloud matrix to obtain user recognition information;

[0063] The performing recognition calculation processing on the vector to be recognized and the standard point cloud matrix to obtain user recognition information includes:

[0064] Perform first distance calculation processing on the vector to be recognized and the standard point cloud matrix to obtain a first distance vector;

[0065] Perform a second distance calculation process on the vector to be recognized and the standard point cloud matrix to obtain a second distance vector;

[0066] Perform a fusion and weighting process on the first distance vector and the second distance vector to obtain a difference vector;

[0067] Determine the user serial number corresponding to the element serial number with the smallest value in the difference vector as the user identification information;

[0068] The expression of the first distance calculation process is:

[0069]

[0070] where a j is the j-th element of the vector to be recognized, A kj is the element in the k-th row and j-th column of the standard point cloud matrix, ξ is a preset adjustment factor, c k represents the k-th element of the first distance vector, and N is the dimension of the vector to be recognized;

[0071] The expression of the second distance calculation process is:

[0072]

[0073] where μ1 and μ2 are preset weighting coefficients, d k represents the k-th element of the second distance vector. The preset weighting coefficients have a value range of [0.1, 0.5].

[0074] The fusion and weighting process has the following calculation expression:

[0075] e k = λ k c k + ν k / d k + η k | c k - d k | ,

[0076] where e k is the k-th element of the difference vector, λ k is the k-th element of the preset first weighting vector, ν k is the k-th element of the preset second weighting vector, η k is the k-th element of the preset third weighting vector, and M is the row dimension of the standard point cloud matrix.

[0077] The acquisition of the dental point cloud dataset is obtained by using a 3D lidar to scan the dental area of the user.

[0078] The user number corresponding to the element number with the smallest value in the difference vector is to determine the element number with the smallest value in the difference vector, which is the recognized user number; the user number is the row number of the row vector of the standard point cloud matrix.

[0079] The data acquisition information includes time information, spatial information, or ability type information corresponding to the data acquisition; when performing autoregressive-moving average modeling, any of the above types of information can be used as the independent variable, and the corresponding data value can be used as the dependent variable.

[0080] The noise reduction process can be implemented using the DeNoise command in MATLAB.

[0081] The point cloud registration process can be implemented using the FastMAC algorithm or the RegFormer algorithm.

[0082] The fusion and weighting process of the first distance vector and the second distance vector to obtain a difference vector includes:

[0083] Performing logarithmic accumulation processing on the standard point cloud matrix to construct a first weighted vector;

[0084] The expression for the logarithmic accumulation processing is:

[0085]

[0086] Performing standard calculation processing on the first distance vector and the second distance vector to construct a second weighted vector;

[0087] The expression for the standard calculation processing is:

[0088]

[0089] Presetting a third weighted vector;

[0090] Performing a weighting process on the first distance vector and the second distance vector to obtain a difference vector; the calculation expression for the weighting process is:

[0091] e k =λ k c k +ν k / d k +η k | c k -d k | ,

[0092] wherein, e k is the k-th element of the difference vector, λ k is the k-th element of the first weighted vector, ν k is the k-th element of the second weighted vector, η k is the k-th element of the preset third weighted vector, and M is the row dimension of the standard point cloud matrix.

[0093] In a second aspect of the embodiments of the present application, a user identity recognition device for dental point cloud data is disclosed. The device includes:

[0094] a memory storing executable program code;

[0095] a processor coupled to the memory;

[0096] The processor calls the executable program code stored in the memory to execute the user identity recognition method for dental point cloud data.

[0097] In a third aspect of the embodiments of the present application, a computer-readable storage medium is disclosed. The computer storage medium stores computer instructions, which are used to execute the user identity recognition method for dental point cloud data when called.

[0098] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed. The information data processing terminal is used to implement the user identity recognition method for dental point cloud data.

[0099] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for user identification of tooth point cloud data, characterized in that: include: S1, obtaining a tooth point cloud dataset and a user tooth standard point cloud dataset; The tooth point cloud data set includes tooth point cloud data; The user tooth standard point cloud data set includes a plurality of tooth point cloud data collected in advance by the user; S2, preprocessing the tooth point cloud dataset to obtain a preprocessed dataset; S3, performing recognition processing on the preprocessed data set and the user's tooth standard point cloud data set to obtain user recognition information; The user identification information is used to represent the user identity information corresponding to the tooth point cloud data set; The identification processing of the preprocessed data set and the user's tooth standard point cloud data set to obtain user identification information includes: S31, representing the preprocessed data set as a vector to be identified; the elements of the vector to be identified are tooth point cloud data; S32, representing the user's tooth standard point cloud data set as a standard point cloud matrix; the row vector of the standard point cloud matrix is ​​the pre-collected tooth point cloud data of each user; the column dimension of the standard point cloud matrix is ​​the same as the dimension of the vector to be identified; the row dimension of the standard point cloud matrix is ​​the number of users; S33, performing identification calculation processing on the vector to be identified and the standard point cloud matrix to obtain user identification information; The step of performing identification calculation processing on the vector to be identified and the standard point cloud matrix to obtain user identification information includes: Performing a first distance calculation process on the vector to be identified and the standard point cloud matrix to obtain a first distance vector; Performing a second distance calculation process on the vector to be identified and the standard point cloud matrix to obtain a second distance vector; Performing weighted fusion processing on the first distance vector and the second distance vector to obtain a difference vector; Determine the user serial number corresponding to the element serial number with the smallest value in the difference vector as the user identification information; The expression of the first distance calculation process is: Among them, a j is the jth element of the vector to be identified, A kj is the element of the kth row and jth column of the standard point cloud matrix, ξ is the preset adjustment factor, c k represents the kth element of the first distance vector, N is the dimension of the vector to be identified; The expression of the second distance calculation process is: Among them, μ1 and μ2 are preset weighting coefficients, d k Represents the kth element of the second distance vector.

2. The method for user identification of tooth point cloud data according to claim 1, characterized in that: The preprocessing of the tooth point cloud data set to obtain a preprocessed data set includes: S21, performing noise reduction processing on the tooth point cloud dataset to obtain a noise reduction dataset; S22, performing anomaly detection processing on the denoised data set to obtain a first data set; S23, performing point cloud registration processing on the first data set to obtain a preprocessed data set.

3. The method for user identification of tooth point cloud data according to claim 2, characterized in that: The performing anomaly detection processing on the denoised data set to obtain a first data set includes: S221, for each type of data attribute of the denoised data set, using data collection information of the data as an independent variable and data values ​​of the data as a dependent variable, perform autoregression-sliding average modeling to obtain regression models of the data attributes of each type; S222, using the regression model, calculating and processing the independent variable to obtain a regression data value; determining whether the absolute value of the difference between the regression data value and the corresponding dependent variable value is greater than a set first regression discrimination threshold; if it is greater than the first regression discrimination threshold, deleting the data from the denoised data set; if it is less than or equal to the first regression discrimination threshold, not processing the data; S223, fusing all data of the denoised data set after executing S221 to S222 to obtain a first data set.

4. The method for user identification of tooth point cloud data according to claim 1, characterized in that: The calculation expression of the fusion weighted processing is: e k =λ k c k +n k / d k +n k |c k -d k |, Among them, e k is the kth element of the difference vector, λ k is the kth element of the preset first weighted vector, ν k is the kth element of the preset second weighted vector, η k is the kth element of the preset third weighted vector.

5. A user identification device for tooth point cloud data, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the user identity recognition method for tooth point cloud data according to any one of claims 1 to 4.

6. A computer storable medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the user identity recognition method for tooth point cloud data according to any one of claims 1 to 4.

7. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the user identity recognition method for tooth point cloud data according to any one of claims 1 to 4.

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