Establishment method and display method of human face model, electronic equipment and medium

Abstract

The embodiment of the invention relates to the technical field of medical instrument digital design, and discloses a human face model building method, a human face model display method, an electronic device and a medium, and the human face model building method comprises the steps: obtaining a registration image; wherein the registration image is a composite image formed by fusing a face side film and a side picture of the patient, or is the face side film of the patient; respectively obtaining a face mesh model and a tooth jaw model; and performing registration fusion on the face mesh model and the tooth jaw model based on the registration image to form a face composite model. Therefore, the face three-dimensional model can be associated with the tooth jaw and appearance data of the patient, so that richer data can be displayed for the user.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of digital design of medical devices, and particularly to a method for establishing a human face model, a display method, an electronic device, and a medium. Background Art

[0002] Orthodontics is a comprehensive treatment method for tooth and oral and maxillofacial deformities, mainly for the correction of tooth and oral and maxillofacial deformities. Its main purpose is to treat abnormal conditions such as irregular teeth, tooth gaps, protruding teeth, and jaw development. Through orthodontic treatment, teeth can become complete and beautiful, achieving the purpose of coordinated balance of the dental and jaw system. In theory, the effect of orthodontics is significant. It can not only restore chewing function, improve oral hygiene, restore facial beauty, but also correct problems such as unclear pronunciation caused by developmental abnormalities, and improve learning and work efficiency. However, due to the long cycle, high cost, and high medical professionalism requirements of orthodontics, it is difficult for patients to solely judge whether the treatment plan is appropriate or whether the treatment effect meets their expectations based on dental and jaw models in the early stage. As a result, some users cannot obtain satisfactory treatment effects after spending time and money, and there is a large sense of expected difference.

[0003] Among the factors affecting patients' expectations, the aesthetics after orthodontics is a highly subjective consideration parameter. Currently, the prediction of the dental and jaw part in this factor has been carried out through technologies such as three-dimensional model reconstruction and digital design of treatment plans, and can be visually displayed through models, providing reliable reference bases for patients, doctors, or professionals. However, the aesthetic appearance is often the main purpose of users' orthodontic treatment. Therefore, we need a method to display the changes in the human face model combined with the dental and jaw, so as to provide richer reference bases for patients, doctors, or other professionals.

[0004] At present, the data of the external appearance part and the internal teeth / jaw part of the human face are collected separately. Unless a dedicated device is used for integrated collection, they cannot be associated and can only be displayed in different forms. The external appearance data cannot be combined in orthognathic / orthodontic design, so that the changes in the external appearance cannot be associated with the changes in the dental and jaw.

[0005] In the present application, the "jaw" in "malocclusion", "open bite", "overbite", "jaw plane", etc. is "occlusion" (hé). This character is a rare character, and since it is not in the general input method character library, it is often written as "jaw" in daily life and on the Internet. For the convenience of reading in this article, it is also written as "jaw". Summary of the Invention

[0006] The purpose of the embodiments of the present application is to provide a method for establishing a human face model, a display method, an electronic device, and a medium, which can associate the dental and jaw of patients with external appearance data, so as to display richer data for users.

[0007] To address the aforementioned technical problems, embodiments of this application provide a method for establishing a facial model, comprising: acquiring a registration image; wherein the registration image is a composite image formed by fusing a lateral view of the patient's face and a profile photograph, or a lateral view of the patient's face; acquiring a facial mesh model and a dental model respectively; and registering and fusing the facial mesh model and the dental model based on the registration image to form the composite facial model.

[0008] An embodiment of this application also provides a method for displaying a human face shape, comprising: obtaining a composite face model according to the above-described method for establishing a human face shape model; and displaying the composite face model on a display device.

[0009] Embodiments of this application also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for establishing a human face model, or to perform the above-described method for displaying a human face model.

[0010] Embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for establishing a human face model, or is capable of executing the above-described method for displaying a human face model.

[0011] The method for establishing a facial model in this embodiment involves registering and fusing a lateral facial image with both dental and maxillofacial features and partial facial contour features with a profile photograph possessing facial contour features. This results in a composite image containing rich dental and maxillofacial features and facial contour features, which is then used as a registration reference for the 3D model. The rich selection of parameters during registration leads to more accurate results. Furthermore, this embodiment can directly use lateral facial images for model registration, simplifying the data, increasing registration speed, and resulting in a highly accurate facial model. Moreover, by fusing facial appearance and intraoral dental and maxillofacial data through the registered image, the external and internal data of the face are correlated. This not only allows for the output of richer facial data but also enables doctors or professionals to collect more parameters from the composite model, while providing more dimensional considerations in orthodontic design.

[0012] In some embodiments, a composite image is formed by fusing a lateral view of the patient's face and a profile photograph. This includes: identifying pre-selected facial contour feature points on the acquired lateral view and profile photograph; and registering and fusing the composite image based on the positions of the facial contour feature points. In this embodiment, facial contour feature points are acquired from various types of images for registration. Since the lateral view of the face simultaneously contains the jawbone and part of the facial contour, the fusion is highly operable, and the resulting composite image has rich feature information and more selectable feature points, facilitating subsequent 3D model registration.

[0013] In some embodiments, the pre-selected facial contour feature points include: the tip of the nose and the protrusions of the upper and lower lips. In this embodiment, the tip of the nose and the protrusions of the upper and lower lips are used as facial contour feature points for registration. These points have obvious features and are more accurately identified during acquisition, so as to ensure the accuracy of registration in subsequent registration.

[0014] In some embodiments, registering and fusing the face mesh model and the dental model based on the registration image to form the face composite model includes: identifying pre-selected facial contour feature points on the face mesh model; identifying dental feature points on the dental model; and registering and fusing the face mesh model and the dental model according to the relationship between the pre-selected dental feature points, the facial contour feature points, and the registration image to form the face composite model.

[0015] In some embodiments, the face mesh model is obtained by: stitching and fusing multi-angle facial photos of the patient; or by scanning the patient's face. This embodiment clarifies multiple methods for obtaining the face mesh model, allowing for the selection of different methods to meet varying needs and expanding the application scenarios of this application.

[0016] In some embodiments, the step of obtaining the face model by stitching and fusing multi-angle facial photos of the patient includes: removing the background from the multi-angle facial photos; performing checkerboard pattern processing on the multi-angle facial photos to achieve calibration and stereo correction, and obtaining camera intrinsic parameters; calculating the depth and three-dimensional coordinates of the face in each facial photo using the parallax information of the multi-angle facial photos and the camera intrinsic parameters; selecting two facial photos taken from different angles as a group for registration, registering at least two different groups of facial photos; fusing the registered groups of facial photos to form a three-dimensional point cloud model; and processing the three-dimensional point cloud model into the facial mesh model. This embodiment clarifies the specific method of obtaining a facial mesh model by stitching and fusing multi-angle facial photos, using checkerboard pattern processing for positioning and correction, making the positioning and correction accurate and feasible, so as to obtain an accurate three-dimensional mesh model through registration.

[0017] In some embodiments, the dental and jaw feature points include: the occlusal contact points of the maxillary first molar and the mandibular first molar, and the occlusal contact points of the maxillary central incisor and the mandibular central incisor. In this embodiment, occlusal contact points are used as the dental and jaw feature points used in registration. The occlusal contact points have obvious features and are accurately identified, which ensures the accuracy and stability of the subsequent registration results.

[0018] In some embodiments, before forming the composite face model, the process includes: scanning and acquiring the texture features of a facial shape model; after forming the composite face model, the process includes: adding the texture features to the composite face model. In this embodiment, textures are added to the facial shape model, making the model's appearance closer to a realistic human face. Attached Figure Description

[0019] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0020] Figure 1 This is a flowchart of a method for creating a human face model according to one embodiment of this application; Figure 2a This is a rendering of a lateral view of a face used in a face shape prediction method according to one embodiment of this application; Figure 2b yes Figure 2a The black and white version; Figure 3 This is a flowchart of a method for obtaining a face mesh model in a method for establishing a face shape model according to one embodiment of this application; Figure 4a This is a schematic diagram of a dental model in a method for creating a human face model according to one embodiment of this application; Figure 4b This is a schematic diagram of a face mesh model in a method for creating a face shape model according to one embodiment of this application; Figure 5 This is a schematic diagram of a composite face model in a method for establishing a face shape model according to one embodiment of this application; Figure 6 This is a diagram showing the alignment of a lateral view with the midline of a composite face model in a face shape prediction method according to one embodiment of this application. Figure 7 This is a schematic diagram of a composite face model with added texture features after deformation in a face shape prediction method according to one embodiment of this application; Figure 8 This is a schematic diagram of an electronic device according to another embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0022] The terms "anterior region" and "posterior region" mentioned in the various embodiments of this application are defined according to the classification of teeth in the 2nd edition of *Introduction to Stomatology*, published by Peking University Medical Press, pages 36-38. The posterior region includes premolars and molars, teeth marked as 4-8 using the FDI notation. The anterior region includes teeth marked as 1-3 using the FDI notation, and the teeth in the anterior region include central incisors, lateral incisors, and canines.

[0023] The “occlusal plane” mentioned in the various embodiments of this application is obtained according to the definition and confirmation method on page 83 of the 6th edition of Orthodontics. One method is to connect the occlusal midpoint of the first permanent molar with the midpoint between the upper and lower central incisors (at the 1 / 2 point of overbite or open bite); the other method is to divide the occlusal contact points of the posterior teeth equally, and the occlusal contact points of the first permanent molar and the first deciduous molar or the first premolar are often used.

[0024] One embodiment of this application relates to a method for establishing a human face model. The specific process of establishing the human face model in this embodiment can be as follows: Figure 1 As shown, it includes: Step 101: Obtain the registration image; wherein the registration image is a composite image formed by fusing a lateral view of the patient's face and a profile photograph, or is a lateral view of the patient's face. Step 102: Obtain the face mesh model and the dental model respectively; Step 103: Based on the registered image, the face mesh model and the dental model are registered and fused to form the face composite model.

[0025] The method for establishing a facial model in this embodiment employs a lateral facial image with both dental and jaw features and partial facial contour features, along with a profile photograph possessing facial contour features, for registration and fusion. This results in a composite image encompassing rich dental and jaw features and facial contour features, which is then used as a registration reference for the 3D model. The rich selection of parameters during registration enhances accuracy. Furthermore, this embodiment can directly use lateral facial images for model registration, simplifying data acquisition, increasing registration speed, and achieving high accuracy in the resulting facial model. The following detailed description of the implementation of the facial model establishment method in this embodiment is provided for ease of understanding and is not essential for implementing this solution.

[0026] It should be noted that the method for establishing the facial model in this embodiment can be implemented through hardware or a combination of computer software and hardware. For hardware implementation, the method for establishing the facial model can be implemented through one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic devices for implementing facial prediction functions, or a selection and combination of the above devices.

[0027] In step 101, the registration image is described as a composite image formed by fusing a lateral view of the patient's face and a profile photo. In one embodiment, the lateral view of the patient's face and the profile photo are acquired, registered, and fused to form a composite image.

[0028] In some embodiments, the lateral facial radiograph and profile photograph can utilize pre-orthodontic data collected by the doctor. For example, the lateral facial radiograph is a projection of the skull at a 90-degree angle to the head, and is the most commonly used X-ray for orthodontic measurements. The original profile photograph is a two-dimensional photograph taken from a side view (left or right), and can be in color or black and white. Based on the profile photograph and the lateral facial radiograph, image fusion and feature recognition methods can be used to register and align the lateral radiograph and profile photograph, and obtain the marked feature points on the teeth (i.e., dentofacial feature points), such as... Figure 2a and Figure 2b Feature points on the dental arch (such as those indicated by label A), or such as Figure 2a and Figure 2b Feature points on the midjaw (as indicated by label B).

[0029] Specifically, this step involves identifying pre-selected facial contour feature points on the acquired lateral facial radiographs and profile photos of the patient; and then registering and fusing these facial contour feature points to form the composite image. In one embodiment, the facial contour feature points on the lateral facial radiographs can be as follows: Figure 2a and Figure 2b The label C indicates this. In this embodiment, facial contour feature points are acquired from various images for registration. Since lateral facial images simultaneously contain both the jaw and part of the facial contour, the fusion process is highly feasible, resulting in a composite image with rich feature information and more selectable feature points, facilitating subsequent 3D model registration. Specifically, the pre-selected facial contour feature points include the tip of the nose and the protrusions of the upper and lower lips. In this embodiment, the tip of the nose and the protrusions of the upper and lower lips are used as facial contour feature points for registration. These points have distinct features and are more accurately identified during acquisition, ensuring registration accuracy in subsequent registration.

[0030] In step 102, the face mesh model and the dental model are obtained respectively.

[0031] In some embodiments, the obtained facial mesh model can be obtained by stitching and fusing multi-angle facial photos of the patient. The stitching and fusing method can be as follows: Figure 3 As shown, the details are as follows: Step 301: Remove the background from face photos taken from multiple angles. In some embodiments, the K-means++ algorithm can be used to remove the background of images taken from different angles. In addition to the K-means++ algorithm mentioned above, other existing tools can also be used for background removal, which will not be listed here.

[0032] Step 302 involves performing checkerboard pattern processing on the multi-angle captured face photos to achieve calibration and stereo correction, thereby obtaining camera intrinsic parameters. In the field of computer vision, camera calibration refers to calculating the camera's intrinsic parameters using computer vision techniques to facilitate subsequent applications such as 3D reconstruction, target tracking, and image processing. The principle of checkerboard calibration utilizes the regular characteristics of a checkerboard pattern in a 3D coordinate system. This step can be divided into two parts: checkerboard image extraction and camera parameter calculation.

[0033] Regarding the chessboard image extraction, some embodiments can be performed as follows: First, prepare a chessboard image, then take a picture of this image with a camera and save it. Next, proceed to the first step—chessboard image extraction. It's important to note that the purpose of chessboard image extraction is to obtain the corner coordinates of the chessboard in the image. This process requires image processing techniques to extract the corner coordinates, including grayscale transformation, image smoothing, and edge detection, which can be determined based on the actual shape and quality of the image. In some embodiments, the image can be converted to grayscale to simplify subsequent image processing operations. Next, the image needs to be smoothed to remove noise. Common smoothing methods include Gaussian filtering and median filtering. Then, edge detection is performed to find the corner coordinates. Classic edge detection algorithms such as the Sobel operator and the Canny operator can be used in this process. Once the corner coordinates are extracted, the second step—camera parameter calculation—can begin.

[0034] Regarding camera parameter calculation, the camera's intrinsic parameters can be calculated using the extracted corner coordinates. Intrinsic parameters include the camera's focal length and optical center. Calculating these intrinsic parameters requires utilizing the coordinate information of the checkerboard grid in a 3D coordinate system. By measuring the side length and number of squares of the checkerboard, its coordinates in the 3D coordinate system can be calculated. Then, using the images captured by the camera and the extracted corner coordinates, a correspondence between pixel coordinates and 3D coordinates can be established, thereby solving for the camera's intrinsic parameters. In some embodiments, the calculation of camera intrinsic parameters can also be combined with the calculation of camera extrinsic parameters, including the camera's rotation and translation matrices. Calculating these extrinsic parameters requires utilizing the coordinate information of the checkerboard grid in a 3D coordinate system and the corner coordinates in the images captured by the camera. By solving for the correspondence between pixel coordinates and 3D coordinates, the camera's rotation and translation matrices, i.e., the camera's extrinsic parameters, can be obtained. Therefore, the calculation of these camera parameters provides fundamental support for subsequent computer vision applications, and calculating both intrinsic and extrinsic parameters can improve the accuracy of subsequent analysis.

[0035] Step 303: Using the disparity information and camera intrinsic parameters of multi-angle face photos, calculate the depth and 3D coordinates of the face in each face photo. Specifically, stereo matching is performed on images from different angles, and the disparity is calculated.

[0036] In some embodiments, after step 303, a point cloud denoising method can also be used to improve the quality of the point cloud data.

[0037] Step 304: Select two face photos taken from different angles as a group for registration, and register at least two different groups of face photos. In some embodiments, the ICP method (Iterative Closest Point) is used to register the two groups of point clouds (e.g., center-left, center-right).

[0038] Step 305: The registered groups of face photos are stitched together to form a 3D point cloud model. In some embodiments, the ICP algorithm is used to stitch together the two groups of point cloud data to form a more comprehensive and consistent point cloud model. Since the data source is face photos, the pixels in the photos can be used as point data to form the point cloud model.

[0039] Step 306: Process the above 3D point cloud model into a face mesh model.

[0040] As can be seen, steps 301-306 above clearly define the specific method of stitching and fusing multi-angle facial photos into a facial mesh model, using checkerboard pattern processing for positioning and correction, making the positioning and correction accurate and feasible, so as to obtain an accurate three-dimensional mesh model through registration.

[0041] Regarding the methods for obtaining the face mesh model, in addition to generating it using the multi-angle face photo stitching and fusion method described above, some embodiments can also directly obtain it by scanning the patient's face. Specifically, existing third-party scanning equipment can be used for the scanning process, which will not be elaborated further here. It is evident that the above embodiments clearly define multiple methods for obtaining the face mesh model, allowing for the selection of different methods to meet different needs and expanding the application scenarios of this application.

[0042] Regarding the acquisition of dental models, if the dental model only includes a portion of the model in the embodiments, it can be obtained by directly scanning the patient's teeth and jaw, or by taking an impression of the patient's teeth and jaw, creating a plaster model, and then scanning the plaster model. If the dental model also includes parts such as the jawbone, it can be obtained by CBCT scanning reconstruction. It is evident that different acquisition methods can be selected based on the actual application scenario, which will not be elaborated further here.

[0043] In some embodiments, the acquired dental model may only include the dentition of the upper and lower jaws, such as... Figure 4a As shown, this method is applied to cases involving only tooth movement, resulting in simplified data for subsequent analysis. It can also include parts of the jawbone and skull in addition to teeth and dentition, making it suitable for cases with both tooth and jaw movement, providing more complete data for subsequent analysis. In some embodiments, the soft tissue portion mainly includes the external appearance, i.e., the outer contour of the soft tissue, such as... Figure 4b As shown above. Figure 4a and Figure 4bThe images are presented in black and white, but in some embodiments, color images may also be used, which will not be listed here.

[0044] It is worth mentioning that the above-mentioned acquisition of the face mesh model and the acquisition of the dental model can be carried out simultaneously or separately. When acquired separately, there is no restriction on the order in which they are acquired.

[0045] In step 103, the face mesh model and the dental model are registered and fused based on the composite image to form the face composite model.

[0046] Regarding the identification of various feature points, some embodiments specifically include: identifying pre-selected facial contour feature points on the face mesh model; identifying dental and jaw feature points on the dental and jaw model; and registering and fusing the face mesh model and the dental and jaw model to form a composite face model based on the relationship between the pre-selected dental and jaw feature points, the facial contour feature points, and the composite image. The identification methods for dental and jaw feature points and facial contour feature points can employ existing methods, such as the method for obtaining landmark points in a cephalometric radiograph submitted by ZhengYa Company on March 21, 2021 (application number: CN202110345400.6), or other feature point identification methods, which will not be listed here. In one embodiment, when acquiring landmark points, the type of point can be pre-selected, such as setting pre-selected occlusal feature points, GO points, etc., or the landmark points can be identified first, and then some points can be selected from the identified landmark points as dental and jaw feature points.

[0047] Regarding the identification of pre-selected facial contour feature points on a facial mesh model, in one embodiment, the facial mesh model is an outer contour model, so feature points representing contour changes can be selected from the facial mesh model, such as lip protrusion points, nose tip points, etc. Those skilled in the art can select according to their needs, and they will not be listed one by one here.

[0048] Regarding registration fusion, spatial alignment can be achieved by identifying the same type of feature points in different image data. Specifically, this is done by fixing one image data set and moving and rotating another image data set to achieve registration. The two sets of image data are then merged to form a single data file. An example is shown below. Figure 5 As shown, it includes the fused dental and jaw model 20 and the face mesh model 10.

[0049] In one embodiment, when registering the lateral view and the face mesh model, the lateral view 30 can be aligned with the face composite model 10 along the center line (e.g., ...). Figure 6As shown in the figure, the positional relationship between the lateral view and the composite face model is relatively clear, so direct midline alignment is performed, which is fast and effective. In one embodiment, after midline alignment, various feature points are registered to quickly register the lateral view and the face mesh model.

[0050] It is worth mentioning that since data obtained from different acquisition methods may have different sizes, the image data can be calibrated before registration. In one embodiment, this can be done as follows: Since point cloud data consists of the pixels in the aforementioned photos, the distance between pixels can be calculated using a set scale length. The scale length refers to the actual length of a straight line segment measured at a certain pixel in the registered and fused image, used to calibrate the actual distance between pixels. The scaling ratio of the two images is calculated based on the actual length of the same object in both types of image data. By scaling and other methods, the distance between the same feature points in different image data is unified, ensuring consistent image size after calibration, avoiding registration errors, and accelerating the registration process.

[0051] As can be seen, this embodiment uses a lateral facial radiograph with both dental and jaw features and partial facial contour features, along with a profile photograph with facial contour features, for registration and fusion to form a composite image containing rich dental and jaw features and facial contour features. This composite image is then used as a registration reference for the 3D model. The rich selection of parameters during registration makes the registration result more accurate. Furthermore, this embodiment can directly use lateral facial radiographs for model registration, which is simple, fast, and produces a highly accurate facial model. Simultaneously, since lateral radiographs and profile photographs are routinely collected data in orthodontic cases, they can be directly extracted and used when needed, simplifying data collection and facilitating the promotion of this application.

[0052] It should be further explained that although the composite image formed by registering and fusing lateral facial images and profile photos is used to register and fuse the facial mesh model and the dental model, in other embodiments, lateral facial images (such as...) can also be used directly. Figure 2a and Figure 2b The image shown is the registration image. The face mesh model and the dental model are registered and fused. Since there are dental and facial contour feature points on the lateral face image, dental and facial contour feature points can be identified on the lateral face image. Then, the face mesh model and the dental model are registered and fused based on these two types of feature points. The registration and fusion process is roughly the same as the above embodiment, and will not be described in detail here.

[0053] Furthermore, before forming the composite face model, the process includes: scanning and acquiring the texture features of a facial shape model; after forming the composite face model, the process includes: adding the texture features to the composite face model. In this embodiment, textures are added to the facial shape model, making the model's appearance closer to a realistic human face, such as... Figure 7 As shown. Additionally, the added textures can be acquired simultaneously during the face mesh model acquisition, reducing the need for additional acquisition steps. It should be noted that... Figure 7 The white frame covering the person's eyes is a privacy protection measure taken to safeguard the individual's portrait rights in the case, and is not a blank area in the established model.

[0054] It should be noted that the examples in the above embodiments are merely illustrative examples for ease of understanding and do not constitute a limitation on the technical solution of the present invention.

[0055] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent. Another embodiment of this application provides a method for displaying facial features. Specifically, a facial feature model can be obtained according to any of the methods for establishing facial feature models in the above embodiments; then, the facial orthodontic prediction model is displayed on a display device. The display device can be any device capable of outputting images, such as a monitor, tablet PC, mobile phone screen, etc., which will not be listed here. Another embodiment of this application relates to an electronic device, such as... Figure 8 As shown, it includes: at least one processor 701; and a memory 702 communicatively connected to the at least one processor 701; wherein the memory 702 stores instructions executable by the at least one processor 701, the instructions being executed by the at least one processor 701 to enable the at least one processor 701 to perform the face model creation method in the above embodiments.

[0056] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0057] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0058] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0059] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for establishing a human face shape model, characterized in that, include: Acquire a registration image; wherein the registration image is a composite image formed by fusing a lateral view of the patient's face and a profile photograph, or is a lateral view of the patient's face; Obtain the face mesh model and the dental model separately; Based on the registered image, the face mesh model and the dental model are registered and fused to form the face composite model.

2. The method for establishing a human face model according to claim 1, characterized in that, A composite image is formed by fusing the patient's lateral facial radiograph and profile photograph, including: Identify pre-selected facial contour feature points on acquired lateral radiographs and profile photos of patients; The composite image is formed by registering and fusing the position of the facial contour feature points.

3. The method for establishing a human face model according to claim 2, characterized in that, The pre-selected facial contour feature points include: the tip of the nose and the protrusions of the upper and lower lips.

4. The method for establishing a human face model according to claim 2, characterized in that, The process of registering and fusing the face mesh model and the dental model based on the registered image to form the composite face model includes: Identify pre-selected facial contour feature points on the facial mesh model; Identify dental and jaw feature points on the dental and jaw model; Based on the relationship between the pre-selected dental and jaw feature points and the facial contour feature points and the registered image, the facial mesh model and the dental and jaw model are registered and fused to form the facial composite model.

5. The method for establishing a human face model according to claim 4, characterized in that, The dental and jaw feature points include: the occlusal contact points of the maxillary first molar and the mandibular first molar, as well as the occlusal contact points of the maxillary central incisor and the mandibular central incisor.

6. The method for establishing a human face model according to claim 1, characterized in that, The face mesh model is obtained through the following methods: Obtained by stitching and fusion of multiple facial photos of the patient from different angles; or... Obtained by scanning the patient's face.

7. The method for establishing a human face shape model according to claim 6, characterized in that, The data obtained by stitching and fusing multi-angle facial photos of the patient includes: Remove the background from photos of faces taken from multiple angles; The camera internal parameters are obtained by performing checkerboard pattern processing on face photos taken from multiple angles to achieve calibration and stereo correction. Using the parallax information of the multi-angle face photos and the camera intrinsic parameters, the depth and three-dimensional coordinates of the face in each face photo are calculated; Select two facial photos taken from different angles as a group for registration, and register at least two different groups of facial photos; The registered groups of face photos are stitched together to form a three-dimensional point cloud model; The 3D point cloud model is processed into the face mesh model.

8. The method for establishing a human face model according to any one of claims 1-7, characterized in that, Before forming the composite face model, the process includes: scanning and acquiring the texture features of the face shape model; After forming the face composite model, the process includes: adding the texture features to the face composite model.

9. A method for displaying facial features, characterized in that, include: A facial composite model is obtained by the method for establishing a facial shape model according to any one of claims 1-8; The composite face model is displayed on a display device.

10. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method for establishing a human face model as described in any one of claims 1 to 8, or to perform the method for displaying a human face model as described in claim 9.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for establishing a human face model as described in any one of claims 1 to 8, or is able to execute the method for displaying a human face as described in claim 9.

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