AI digital human modeling method and system based on user portrait

Abstract

The invention relates to the technical field of digital human modeling, particularly discloses an AI digital human modeling method and system based on a user portrait, and is used for solving the problems that an existing digital human is difficult to match with current appearance features of a user, and the system cannot accurately identify the user. The method comprises the following steps: acquiring a first facial feature and a first digital feature parameter of a user, constructing a first-level distinguishing feature analysis model to acquire a first distinguishing feature parameter of the user, first two-dimensional facial data information based on the user, and first digital human facial information generated based on the first two-dimensional facial data information; and obtaining a second distinguishing feature parameter of the user, constructing a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain a user facial matching index, performing user facial change recognition matching, and performing marking early warning on the user difficult to match. Accurate recognition and dynamic tracking of facial health and senescence features of the user are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of digital human modeling, and more specifically, to an AI digital human modeling method and system based on user portrait. Background Art

[0002] In the long-term medical and health management scenario, AI digital human modeling based on user portraits is widely used in facial recognition, pathological analysis and personalized health consultation. When a user first establishes a health record, a three-dimensional digital human of the user is generated through the uploaded two-dimensional facial image to identify user characteristics and track health changes. However, as the user ages, his facial features (such as sagging skin, deepening wrinkles, and changes in fat distribution) gradually change significantly, and the generated digital human model is based on raw data and lacks the ability to dynamically model the natural aging process. For example, a user uploads a facial image generated by a digital human at the age of 30 to manage facial health. However, as the user ages to 35, his skin becomes more sagging, his eye bags become obvious, and his nasolabial folds deepen. However, the system still relies on the digital human model generated by the initial image 5 years ago and fails to update dynamically. This difference makes it difficult for the digital human to match the user's current appearance features, and the system cannot accurately identify the user. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides an AI digital human modeling method and system based on user portrait, which is used to solve the problem that the existing digital human is difficult to match the user's current appearance features and the system cannot accurately identify the user, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: An AI digital human modeling method based on user portrait includes the following steps: Step 1, obtaining the original two-dimensional facial data information of the user and the original digital human facial information generated based on the two-dimensional facial data information, performing recognition processing on the original two-dimensional data information, obtaining the first facial feature parameter of the user, and obtaining the first digital feature parameter based on the original digital human facial information; Step 2: construct a first-level distinguishing feature analysis model based on the first facial feature and the first digital feature parameter to obtain the first distinguishing feature parameter of the user; the first distinguishing feature parameter includes a first facial aging feature parameter and a first health feature parameter; the formula of the first-level distinguishing feature analysis model is: ; ; Where: is the first facial aging characteristic parameter, is the first health characteristic parameter, is the distance between the user's two eye corners, The distance between the user's left eye corner and nose tip, is the distance between the user's right eye corner and nose tip, is the distance between the user's left mouth corner and nose tip, is the distance between the user's right mouth corner and nose tip, is the pixel value of the i-th point in the original two-dimensional facial data information, is the first distribution coefficient, is the first eigenvalue coefficient, is the number of pixels in the original two-dimensional facial data information; Step 3, obtaining the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information, and processing the first two-dimensional facial data information and the first digital human facial information respectively to obtain the user's second distinguishing feature parameter; Step 4: construct a facial feature change matching model based on the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user facial matching index, perform user facial change recognition and matching, and mark and warn users who are difficult to match.

[0005] As a further solution of the present invention, in step 1, the original two-dimensional facial data information of the user and the original digital human information generated based on the two-dimensional facial data information are obtained, the original two-dimensional data information is identified and processed, and the first facial feature parameters of the user are obtained, and the first facial feature parameters include the pixel values ​​of the original two-dimensional facial data information, the first distribution coefficient and the first feature mean coefficient: the front two-dimensional facial image of the user in the original two-dimensional facial data information is obtained, the front two-dimensional facial image of the user is segmented, and the front two-dimensional facial image is segmented into a number of facial feature areas, a number of feature points are selected in each facial feature area, the pixel values ​​of all feature points are calculated for variance to obtain the first distribution coefficient of each facial feature area, and the pixel values ​​of all feature points are summed and the average value is calculated to obtain the first feature mean coefficient.

[0006] As a further solution of the present invention, in step 1, a first digital feature parameter is obtained based on the original digital human facial information: the original digital human facial information is obtained, and the three-dimensional coordinates of the key points of the original digital human facial information are located, namely, the three-dimensional coordinates of the user's eye corners, the three-dimensional coordinates of the nose tip, and the three-dimensional coordinates of the mouth corners, and the distance between the user's two eye corners, the distance between the left and right eye corners and the nose tip, and the distance between the left and right mouth corners and the nose tip are respectively calculated and obtained as the first digital feature parameter.

[0007] As a further solution of the present invention, in step 3, the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information are obtained, and the first two-dimensional facial data information and the first digital human facial information are processed respectively to obtain the user's second distinguishing feature parameters: the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information are obtained, the first two-dimensional facial data information is identified and processed to obtain the user's second facial feature parameters, and the second digital feature parameters are obtained based on the first digital human facial information; a secondary distinguishing feature analysis model is constructed based on the second facial features and the second digital feature parameters to obtain the user's second distinguishing feature parameters; the second distinguishing feature parameters include a second facial aging feature parameter and a second health feature parameter; the formula of the secondary distinguishing feature analysis model is: ; ; Where: is the second facial aging characteristic parameter, is the second health characteristic parameter, is the second distance between the two corners of the user's eyes, The second distance between the user's left eye corner and nose tip, is the second distance between the user's right eye corner and nose tip, is the second distance between the user's left mouth corner and nose tip, is the pixel value of the i-th point in the first two-dimensional facial data information, is the second distribution coefficient, is the second characteristic mean coefficient, is the number of pixels in the first two-dimensional facial data information.

[0008] As a further solution of the present invention, step 4, constructing a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain a user facial matching index, performing user facial change recognition matching, and marking and warning users with matching difficulties: obtaining a first facial aging feature parameter and a first health feature parameter in the first distinguishing feature parameter, a second facial aging feature parameter and a second health feature parameter in the second distinguishing feature parameter, constructing a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain a user facial matching index, the formula of the facial feature change matching model is: ; Where: is the user's facial matching indicator, is the first facial aging characteristic parameter, is the first health characteristic parameter, is the second facial aging characteristic parameter, is the second health characteristic parameter; Obtain a user facial matching index, and compare the user facial matching index with a preset facial matching threshold. If the user facial matching index is greater than or equal to the preset facial matching threshold, mark the user facial image as difficult to recognize; if the user facial matching index is less than the preset facial matching threshold, there is no need to mark the user facial image as difficult to recognize.

[0009] An AI digital human modeling system based on user portrait is used to implement the above-mentioned AI digital human modeling method based on user portrait, including a processor and a data acquisition and feature extraction module connected to the processor in communication, a first distinguishing feature parameter analysis module, a second distinguishing feature parameter analysis module, a facial feature change matching evaluation module and a matching warning module; the data acquisition and feature extraction module is used to obtain the user's original two-dimensional facial data information and the original digital human facial information generated based on the two-dimensional facial data information, identify and process the original two-dimensional data information, obtain the user's first facial feature parameter, and obtain the first digital feature parameter based on the original digital human facial information; the first distinguishing feature parameter analysis module A first-level distinguishing feature analysis model is constructed based on the first facial feature and the first digital feature parameter to obtain the user's first distinguishing feature parameter; the first distinguishing feature parameter analysis module is used to obtain the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information, and the first two-dimensional facial data information and the first digital human facial information are processed respectively to obtain the user's second distinguishing feature parameter; the facial feature change matching evaluation module is used to construct a facial feature change matching model based on the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user's facial matching index, and perform user facial change recognition and matching; the matching warning module is used to mark and warn users who are difficult to match.

[0010] The technical effects and advantages of the AI ​​digital human modeling method and system based on user portrait of the present invention are as follows: the present invention obtains the user's first facial feature and first digital feature parameter, constructs a first-level distinguishing feature analysis model to obtain the user's first distinguishing feature parameter, based on the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information, realizes the update of the user's facial features that change over time, so that the digital human model can be continuously adjusted and optimized with the user's actual state; obtains the user's second distinguishing feature parameter, constructs a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user's facial matching index, performs user facial change recognition and matching, and marks and warns users with matching difficulties, so as to help users more intuitively understand the changing trend of facial status, and realize accurate recognition and dynamic tracking of the user's facial health and aging features. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The figure is a flow chart of an AI digital human modeling method based on user portrait in the present invention.

[0012] Figure 2 This is a structural schematic diagram of an AI digital human modeling system based on user portraits in the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0014] Figure 1 The figure shows a flow chart of a method for AI digital human modeling based on user portrait provided by the first embodiment of the present invention. Figure 1 As shown, an AI digital human modeling method based on user portrait in this embodiment includes the following steps: Step 1, obtaining the original two-dimensional facial data information of the user and the original digital human facial information generated based on the two-dimensional facial data information, performing recognition processing on the original two-dimensional data information, obtaining the first facial feature parameter of the user, and obtaining the first digital feature parameter based on the original digital human facial information; Step 2: construct a first-level distinguishing feature analysis model based on the first facial feature and the first digital feature parameter to obtain the first distinguishing feature parameter of the user; the first distinguishing feature parameter includes a first facial aging feature parameter and a first health feature parameter; Step 3, obtaining the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information, and processing the first two-dimensional facial data information and the first digital human facial information respectively to obtain the user's second distinguishing feature parameter; Step 4: construct a facial feature change matching model based on the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user facial matching index, perform user facial change recognition and matching, and mark and warn users who are difficult to match.

[0015] It should be noted that the first two-dimensional facial data information refers to a two-dimensional facial image of a user obtained after the original two-dimensional facial data information is obtained.

[0016] Using the user's two-dimensional facial data information, multiple facial structural parameters are calculated, and the first facial aging feature parameter and the first health feature parameter are generated. The geometric structure, symmetry and pixel distribution of the face are analyzed, and the aging features and health status are quantified; not only is a digital human model generated based on the initial two-dimensional image, but the two-dimensional facial data is also re-collected at subsequent time nodes, and new digital human information is generated. By comparing the first distinguishing feature parameter and the second distinguishing feature parameter, this method realizes the update of the user's facial features that change over time, so that the digital human model can be continuously adjusted and optimized according to the user's actual state. Through dynamic updates, the user's facial state can be continuously monitored, allowing the system to identify significant changes in a timely manner. , providing users with real-time health status feedback; the facial feature change matching model constructed based on the first distinguishing feature parameter and the second distinguishing feature parameter can compare the feature differences between two time nodes, quantify the user's facial changes through the user's facial matching index, and accurately identify the significant change areas, helping users to more intuitively understand the changing trends of facial status. For users with matching difficulties, the system can automatically mark and warn, prompting the need to re-collect data or further adjust the model to ensure the accuracy and applicability of the model; the feature parameters of the two-dimensional image (such as pixel distribution and structural characteristics) and the geometric parameters of the three-dimensional digital human model (such as facial curvature and symmetry) are obtained, realizing multimodal data fusion.

[0017] Specifically, in step 1, the original two-dimensional facial data information of the user and the original digital human information generated based on the two-dimensional facial data information are obtained, the original two-dimensional data information is identified and processed, and the first facial feature parameters of the user are obtained, and the first facial feature parameters include the pixel value of the original two-dimensional facial data information, the first distribution coefficient and the first feature mean coefficient: the front two-dimensional facial image of the user in the original two-dimensional facial data information is obtained, the front two-dimensional facial image of the user is segmented, and the front two-dimensional facial image is segmented into a number of facial feature areas, a number of feature points are selected in each facial feature area, the variance of the pixel values ​​of all feature points is calculated to obtain the first distribution coefficient of each facial feature area, and the pixel values ​​of all feature points are summed and the average value is calculated to obtain the first feature mean coefficient.

[0018] Specifically, in step 1, the first digital feature parameter is obtained based on the original digital human facial information: the original digital human facial information is obtained, and the three-dimensional coordinates of the key points of the original digital human facial information are located, namely, the three-dimensional coordinates of the user's eye corners, the three-dimensional coordinates of the nose tip, and the three-dimensional coordinates of the mouth corners, and the distance between the user's two eye corners, the distance between the left and right eye corners and the nose tip, and the distance between the left and right mouth corners and the nose tip are respectively calculated and obtained as the first digital feature parameter.

[0019] The calculation formula of the first digital characteristic parameter is: ; ; ; ; ; Where: is the distance between the user's two eye corners, The distance between the user's left eye corner and nose tip, is the distance between the user's right eye corner and nose tip, is the distance between the user's left mouth corner and nose tip, is the distance between the user's right mouth corner and nose tip, is the 3D coordinate of the user’s left eye corner, is the 3D coordinate of the user’s right eye corner, is the 3D coordinate of the user’s nose tip, is the 3D coordinate of the user’s left mouth corner, is the 3D coordinate of the user's right mouth corner.

[0020] By performing regional segmentation and pixel feature extraction on two-dimensional facial images, independent analysis of features of each facial region is ensured. By calculating the variance (first distribution coefficient) and mean (first characteristic mean coefficient) of pixel values, the texture features of the facial region (such as wrinkles and skin smoothness) are quantified. The variance reflects the amplitude of pixel changes in the region and captures the complexity of facial texture (such as wrinkle depth). The mean reflects the overall brightness of the region and helps identify skin color distribution and glossiness. By comparing the distance between the left and right eye corners and the nose tip, and the distance between the left and right mouth corners and the nose tip, the symmetry of the upper and lower parts of the face can be quantified, helping to discover changes in facial symmetry caused by aging or health problems. The distance between the two eye corners is calculated, and combined with the distance between the eye corners and the nose tip and the distance between the mouth corners and the nose tip, a quantitative analysis of facial proportions can be provided.

[0021] Specifically, a primary distinguishing feature analysis model is constructed based on the first facial feature and the first digital feature parameter to obtain the first distinguishing feature parameter of the user. The formula of the primary distinguishing feature analysis model is: ; ; Where: is the first facial aging characteristic parameter, is the first health characteristic parameter, is the distance between the user's two eye corners, The distance between the user's left eye corner and nose tip, is the distance between the user's right eye corner and nose tip, is the distance between the user's left mouth corner and nose tip, is the distance between the user's right mouth corner and nose tip, is the pixel value of the i-th point in the original two-dimensional facial data information, is the first distribution coefficient, is the first eigenvalue coefficient, is the number of pixels in the original two-dimensional facial data information;

[0022] It comprehensively considers the changes in the geometric proportions of the user's face and accurately quantifies the changes in facial symmetry. Through the relative positions of the key points of the corners of the eyes, the tip of the nose and the corners of the mouth, it can capture significant features such as decreased symmetry and facial sagging during facial aging. Through the changing trends of the geometric parameters in the formula, it can objectively evaluate the degree of facial aging, such as the formation of eye bags, deepening of nasolabial folds or facial collapse, which helps to identify small but important feature changes, especially early signs of aging in symmetry and sagging; by analyzing the differences between pixels and distribution coefficients, it can detect skin abnormalities, such as decreased smoothness, pigmentation or abnormal spot distribution. It can sensitively capture subtle changes in health features, giving the system higher recognition accuracy.

[0023] It should be noted that in step 3, the first two-dimensional facial data information of the user and the first digital human facial information generated based on the first two-dimensional facial data information are obtained, and the first two-dimensional facial data information and the first digital human facial information are processed respectively to obtain the second distinguishing feature parameters of the user: the first two-dimensional facial data information of the user and the first digital human facial information generated based on the first two-dimensional facial data information are obtained, the first two-dimensional facial data information is identified and processed to obtain the second facial feature parameters of the user, and the second digital feature parameters are obtained based on the first digital human facial information; a secondary distinguishing feature analysis model is constructed based on the second facial features and the second digital feature parameters to obtain the second distinguishing feature parameters of the user; the second distinguishing feature parameters include a second facial aging feature parameter and a second health feature parameter; the formula of the secondary distinguishing feature analysis model is: ; ; Where: is the second facial aging characteristic parameter, is the second health characteristic parameter, is the second distance between the two corners of the user's eyes, The second distance between the user's left eye corner and nose tip, is the second distance between the user's right eye corner and nose tip, is the second distance between the user's left mouth corner and nose tip, is the pixel value of the i-th point in the first two-dimensional facial data information, is the second distribution coefficient, is the second characteristic mean coefficient, is the number of pixels in the first two-dimensional facial data information.

[0024] Specifically, the second facial feature parameters include pixel values, a second distribution coefficient and a second feature mean coefficient of the first two-dimensional facial data information: obtain a frontal two-dimensional facial image of the user in the first two-dimensional facial data information, perform segmentation processing on the frontal two-dimensional facial image of the user, segment the frontal two-dimensional facial image into a number of facial feature areas, select a number of feature points in each facial feature area, perform variance calculation on the pixel values ​​of all feature points to obtain the second distribution coefficient of each facial feature area, and sum up the pixel values ​​of all feature points to calculate the average value to obtain the second feature mean coefficient.

[0025] Obtain the second digital feature parameter based on the first digital person's facial information: obtain the first digital person's facial information, and locate the three-dimensional coordinates of the key points of the first digital person's facial information, namely, the three-dimensional coordinates of the user's eye corners, the three-dimensional coordinates of the nose tip, and the three-dimensional coordinates of the mouth corners, and respectively calculate and obtain the second distance between the user's two eye corners, the second distance between the left and right eye corners and the nose tip, and the second distance between the left and right mouth corners and the nose tip as the second digital feature parameters.

[0026] The calculation formula of the second digital characteristic parameter is: ; ; ; ; ; Where: is the second distance between the two corners of the user's eyes, The second distance between the user's left eye corner and nose tip, is the second distance between the user's right eye corner and nose tip, is the second distance between the user's left mouth corner and nose tip, is the second distance between the user's right mouth corner and nose tip, is the three-dimensional coordinate of the left eye corner of the first digital human facial information, is the three-dimensional coordinate of the right eye corner of the first digital human facial information, is the three-dimensional coordinate of the nose tip of the first digital human facial information, is the three-dimensional coordinate of the left corner of the mouth of the first digital human face information, is the three-dimensional coordinate of the right mouth corner of the first digital human facial information.

[0027] The combination of two-dimensional pixel features and three-dimensional spatial coordinate features makes the description of facial features more comprehensive, capturing both texture information and geometric structure changes. The variance and mean of pixel values ​​are calculated in each facial feature area to effectively quantify the uniformity of facial texture and the differences between regions, enhancing the accuracy of feature extraction. The geometric distance calculation based on three-dimensional coordinates can reflect the dynamic changes of the user's facial geometric structure. Regularly updating the user's two-dimensional facial information and the three-dimensional key point data of the digital human can continuously track the changing trend of facial features. The dynamic changes of the second distribution coefficient (regional variance) and the second feature mean coefficient can be refined to the degree of texture aging in a specific area. The division of facial feature areas (such as eyes, nose, and mouth) facilitates regional optimization of the model to avoid the limitations of a single feature. The second distribution coefficient can quantify the unevenness of texture and brightness distribution in the area, indirectly reflecting the changes in skin texture during the aging process. The second feature mean coefficient provides the overall texture brightness information in the area, which can be combined with medical data to analyze health problems. The key point spacing (such as eye spacing, distance between mouth corners and nose tip) reflects the changing trend of facial bones and soft tissue structures, which helps to evaluate facial structural changes (such as facial fat loss).

[0028] Specifically, step 4, constructing a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain a user facial matching index, performing user facial change recognition matching, and marking and warning users with matching difficulties: obtaining a first facial aging feature parameter and a first health feature parameter in the first distinguishing feature parameter, a second facial aging feature parameter and a second health feature parameter in the second distinguishing feature parameter, constructing a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain a user facial matching index, the formula of the facial feature change matching model is: ; Where: is the user's facial matching indicator, is the first facial aging characteristic parameter, is the first health characteristic parameter, is the second facial aging characteristic parameter, is the second health characteristic parameter; Obtain a user facial matching index, and compare the user facial matching index with a preset facial matching threshold. If the user facial matching index is greater than or equal to the preset facial matching threshold, mark the user facial image as difficult to recognize; if the user facial matching index is less than the preset facial matching threshold, there is no need to mark the user facial image as difficult to recognize.

[0029] Facial aging features and health features are simultaneously included in the matching index calculation to comprehensively reflect the overall changes in the user's face. By comparing the changes in the two independent parts, the accurate quantification of the change trend is ensured; the user's facial matching index is obtained, and the user's facial matching index is compared with the preset facial matching threshold. If the user's facial matching index is greater than or equal to the preset facial matching threshold, the user's facial image is marked as difficult to recognize; if the user's facial matching index is less than the preset facial matching threshold, there is no need to mark the user's facial image as difficult to recognize; the facial feature differences at different time points (the first distinguishing feature parameter and the second distinguishing feature parameter) are included in the matching model, which can dynamically reflect the user's facial changes, provide the user's facial health and aging changing trends, and lay the foundation for long-term facial status management.

[0030] Figure 2 A structural schematic diagram of an AI digital human modeling system based on user portrait provided in Embodiment 2 of the present invention is shown. An AI digital human modeling system based on user portrait in this embodiment is used to implement the above-mentioned AI digital human modeling method based on user portrait, including a processor and a data acquisition and feature extraction module, a first distinguishing feature parameter analysis module, a second distinguishing feature parameter analysis module, a facial feature change matching evaluation module and a matching warning module connected to the processor in communication; the data acquisition and feature extraction module is connected to the first distinguishing feature parameter analysis module, the first distinguishing feature parameter analysis module and the second distinguishing feature parameter analysis module are respectively connected to the facial feature change matching evaluation module, and the facial feature change matching evaluation module is connected to the matching warning module; the data acquisition and feature extraction module is used to obtain the user's original two-dimensional facial data information and the original digital human modeling method generated based on the two-dimensional facial data information. The digital human facial information is identified and processed, and the first facial feature parameters of the user are obtained, and the first digital feature parameters are obtained based on the original digital human facial information; the first distinguishing feature parameter analysis module constructs a first-level distinguishing feature analysis model based on the first facial feature and the first digital feature parameter to obtain the first distinguishing feature parameter of the user; the first distinguishing feature parameter analysis module is used to obtain the first two-dimensional facial data information of the user and the first digital human facial information generated based on the first two-dimensional facial data information, and the first two-dimensional facial data information and the first digital human facial information are processed respectively to obtain the second distinguishing feature parameters of the user; the facial feature change matching evaluation module is used to construct a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user's facial matching index, and perform user facial change recognition matching; the matching warning module is used to mark and warn users with matching difficulties.

[0031] The embodiment of the present invention obtains the user's first facial feature and first digital feature parameter, constructs a first-level distinguishing feature analysis model to obtain the user's first distinguishing feature parameter, and based on the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information, realizes the update of the user's facial features that change over time, so that the digital human model can be continuously adjusted and optimized with the user's actual state; obtains the user's second distinguishing feature parameter, constructs a facial feature change matching model according to the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user's facial matching index, performs user facial change recognition and matching, and marks and warns users with matching difficulties, helping users to more intuitively understand the changing trend of facial status, and realizing accurate recognition and dynamic tracking of the user's facial health and aging features.

[0032] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0033] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An AI digital human modeling method based on user portrait, characterized in that: The steps include: Step 1, obtaining the original two-dimensional facial data information of the user and the original digital human facial information generated based on the two-dimensional facial data information, performing recognition processing on the original two-dimensional data information, obtaining the first facial feature parameter of the user, and obtaining the first digital feature parameter based on the original digital human facial information; Step 2: construct a first-level distinguishing feature analysis model based on the first facial feature and the first digital feature parameter to obtain the first distinguishing feature parameter of the user; the first distinguishing feature parameter includes a first facial aging feature parameter and a first health feature parameter; the formula of the first-level distinguishing feature analysis model is: ； ； Where: is the first facial aging characteristic parameter, is the first health characteristic parameter, is the distance between the user's two eye corners, The distance between the user's left eye corner and nose tip, is the distance between the user's right eye corner and nose tip, is the distance between the user's left mouth corner and nose tip, is the distance between the user's right mouth corner and nose tip, is the pixel value of the i-th point in the original two-dimensional facial data information, is the first distribution coefficient, is the first eigenvalue coefficient, is the number of pixels in the original two-dimensional facial data information; Step 3, obtaining the user's first two-dimensional facial data information and the first digital human facial information generated based on the first two-dimensional facial data information, and processing the first two-dimensional facial data information and the first digital human facial information respectively to obtain the user's second distinguishing feature parameter; Step 4: construct a facial feature change matching model based on the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user facial matching index, perform user facial change recognition and matching, and mark and warn users who are difficult to match.

2. The AI ​​digital human modeling method based on user portrait according to claim 1 is characterized in that: In step 1, the original two-dimensional facial data information of the user and the original digital human information generated based on the two-dimensional facial data information are obtained, the original two-dimensional data information is identified and processed, and the first facial feature parameters of the user are obtained, and the first facial feature parameters include the pixel value of the original two-dimensional facial data information, the first distribution coefficient and the first feature mean coefficient: the front two-dimensional facial image of the user in the original two-dimensional facial data information is obtained, the front two-dimensional facial image of the user is segmented, and the front two-dimensional facial image is segmented into a number of facial feature areas, a number of feature points are selected in each facial feature area, the pixel values ​​of all feature points are calculated by variance to obtain the first distribution coefficient of each facial feature area, and the pixel values ​​of all feature points are summed and the average value is calculated to obtain the first feature mean coefficient.

3. The AI ​​digital human modeling method based on user portrait according to claim 1 is characterized in that: In step 1, the first digital feature parameter is obtained based on the original digital human facial information: the original digital human facial information is obtained, and the three-dimensional coordinates of the key points of the original digital human facial information are located, namely, the three-dimensional coordinates of the user's eye corners, the three-dimensional coordinates of the nose tip, and the three-dimensional coordinates of the mouth corners, and the distance between the user's two eye corners, the distance between the left and right eye corners and the nose tip, and the distance between the left and right mouth corners and the nose tip are respectively calculated and obtained as the first digital feature parameter.

4. The AI ​​digital human modeling method based on user portrait according to claim 1 is characterized in that: In step 3, the first two-dimensional facial data information of the user and the first digital human facial information generated based on the first two-dimensional facial data information are obtained, and the first two-dimensional facial data information and the first digital human facial information are processed respectively to obtain the second distinguishing feature parameters of the user: the first two-dimensional facial data information of the user and the first digital human facial information generated based on the first two-dimensional facial data information are obtained, the first two-dimensional facial data information is identified and processed to obtain the second facial feature parameters of the user, and the second digital feature parameters are obtained based on the first digital human facial information; a secondary distinguishing feature analysis model is constructed based on the second facial features and the second digital feature parameters to obtain the second distinguishing feature parameters of the user; the second distinguishing feature parameters include a second facial aging feature parameter and a second health feature parameter; the formula of the secondary distinguishing feature analysis model is: ； ； Where: is the second facial aging characteristic parameter, is the second health characteristic parameter, is the second distance between the two corners of the user's eyes, The second distance between the user's left eye corner and nose tip, is the second distance between the user's right eye corner and nose tip, is the second distance between the user's left mouth corner and nose tip, is the pixel value of the i-th point in the first two-dimensional facial data information, is the second distribution coefficient, is the second characteristic mean coefficient, is the number of pixels in the first two-dimensional facial data information.

5. The AI ​​digital human modeling method based on user portrait according to claim 1 is characterized in that: Step 4, constructing a facial feature change matching model based on the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user's facial matching index, performing user facial change recognition and matching, and marking and warning users with matching difficulties: obtaining the first facial aging feature parameter and the first health feature parameter in the first distinguishing feature parameter, the second facial aging feature parameter and the second health feature parameter in the second distinguishing feature parameter, constructing a facial feature change matching model based on the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user's facial matching index, the formula of the facial feature change matching model is: ； Where: is the user's facial matching indicator, is the first facial aging characteristic parameter, is the first health characteristic parameter, is the second facial aging characteristic parameter, is the second health characteristic parameter; Obtain a user facial matching index, and compare the user facial matching index with a preset facial matching threshold. If the user facial matching index is greater than or equal to the preset facial matching threshold, mark the user facial image as difficult to recognize; if the user facial matching index is less than the preset facial matching threshold, there is no need to mark the user facial image as difficult to recognize.

6. An AI digital human modeling system based on user portrait, used to implement an AI digital human modeling method based on user portrait according to any one of claims 1 to 5, characterized in that: It includes a processor and a data acquisition and feature extraction module, a first distinguishing feature parameter analysis module, a second distinguishing feature parameter analysis module, a facial feature change matching evaluation module and a matching warning module connected to the processor in communication; the data acquisition and feature extraction module is used to obtain the original two-dimensional facial data information of the user and the original digital human facial information generated based on the two-dimensional facial data information, identify and process the original two-dimensional data information, obtain the first facial feature parameter of the user, and obtain the first digital feature parameter based on the original digital human facial information; the first distinguishing feature parameter analysis module constructs a first-level distinguishing feature analysis model based on the first facial feature and the first digital feature parameter, and obtains the first distinguishing feature parameter of the user; the first distinguishing feature parameter analysis module is used to obtain the first two-dimensional facial data information of the user and the first digital human facial information generated based on the first two-dimensional facial data information, and processes the first two-dimensional facial data information and the first digital human facial information respectively to obtain the second distinguishing feature parameter of the user; The facial feature change matching assessment module is used to construct a facial feature change matching model based on the first distinguishing feature parameter and the second distinguishing feature parameter to obtain the user facial matching index and perform user facial change recognition and matching; the matching warning module is used to mark and warn users who are difficult to match.

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