Facial contour data processing method and device, and electronic device

Abstract

The present application provides a face contour data processing method and device and electronic equipment, by fitting a plurality of first face contour key point data into at least two groups of quadratic curves, thereby converting the face contour key point data of relatively independent points into a curve function capable of expressing continuous transformation. In this way, in the subsequent interpolation or face shape adjustment process, the semantic error of the adjustment action of the newly added points or point positions in the interpolation can be reduced, and the accuracy of face shape adjustment or face shape setting can be improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and more specifically, to a method, apparatus, and electronic device for processing facial contour data. Background Technology

[0002] In facial image processing scenarios such as face shape adjustment and setting, it is often necessary to adjust or warp the positions of key points on the facial contour. Existing technologies typically first identify multiple sparse facial contour key points through facial landmark recognition, and then adjust the positions of these sparse key points through mapping. However, in this type of adjustment, each sparse facial contour key point exists relatively independently with no semantic connection. The adjustment process usually fails to consider whether the curve formed by connecting the sparse facial contour key points as a whole is aesthetically pleasing, and this one-to-one mapping facial adjustment rule may cause facial distortion problems when viewing faces at large angles. Summary of the Invention

[0003] To overcome the aforementioned shortcomings in the prior art, the purpose of this application is to provide a facial contour data processing method, the method comprising: Obtain the face image to be processed; Key point recognition is performed on the face image to be processed to obtain multiple first face shape contour key points; Based on the key points of the first facial contour at different locations, at least two sets of quadratic functions are subjected to curve fitting to obtain at least two sets of facial contour curves.

[0004] In one possible implementation, the method further includes: Interpolation processing is performed on the first facial contour key points based on at least two sets of facial contour curves to obtain multiple second facial contour key points.

[0005] In one possible implementation, the step of interpolating the key points of the first facial contour based on at least two sets of facial contour curves includes: Use the first facial contour key point as the reference point; Determine the tangent line of the facial contour curve corresponding to the reference point at the reference point; Determine the intersection point of the perpendicular line on the tangent line at a predetermined distance from the reference point and the facial contour curve; The intersection point is determined as the key point of the second facial contour obtained by interpolation, and the intersection point is used as the new reference point for further interpolation.

[0006] In one possible implementation, the step of interpolating the key points of the first facial contour based on at least two sets of facial contour curves includes: Determine the facial contour curve corresponding to two adjacent first facial contour key points, and determine multiple intermediate points on the straight line connecting the two first facial contour key points. The intersection of the perpendicular line drawn from the line connecting the two points at each of the intermediate points with the face contour curve is determined as the second face contour key point obtained by interpolation.

[0007] In one possible implementation, the method further includes: Adjust the parameters in the functions corresponding to at least two sets of the facial contour curves to obtain the adjusted facial contour curves; Based on the adjusted facial contour curve, the facial image to be processed is adjusted.

[0008] In one possible implementation, the step of performing curve fitting on at least two sets of quadratic functions based on the first facial contour key points at different locations to obtain at least two sets of facial contour curves includes: For the first target key point that identifies the lower jaw of the face in the first face contour key point, the first loss value is determined by the least squares method based on the first face contour curve. For the second target key points other than the first target key points in the first face shape contour key points, a second loss value is determined based on the second face shape contour curve; Based on the first target key point and the second target key point, the function parameters corresponding to the first face contour curve and the second face contour curve are solved with the aim of minimizing the sum of the first loss value and the second loss value.

[0009] In one possible implementation, the first facial contour key points include points 0 to K arranged sequentially, wherein points n to m are the first target key points, and points 0 to n and points m to K are the second target key points; The step of determining a first loss value based on the first facial contour curve for the first target key point that identifies the lower jaw portion of the face among the first facial contour key points includes: The first loss value is determined according to the following formula. :

[0010]

[0011]

[0012]

[0013] in,( , () represents the coordinate pair from the nth first facial contour key point to the mth first facial contour key point. , , Let be the function parameters to be solved for the first facial contour curve. The function parameters to be solved are common to both the first and second face contour curves. The step of determining a second loss value based on a second facial contour curve for other second target key points besides the first target key points in the first facial contour key points includes: The second loss value is determined according to the following formula. :

[0014]

[0015]

[0016]

[0017] in,( , () represents the coordinate pairs from the 0th first facial contour key point to the nth first facial contour key point and from the mth first facial contour key point to the Kth first facial contour key point. , , , where is the function parameter to be solved for the second facial contour curve.

[0018] Another object of this application is to provide a facial contour data processing device, the facial contour data processing device comprising: The data acquisition module is used to acquire the face image to be processed; The key point recognition module is used to perform key point recognition on the face image to be processed and obtain multiple first face shape contour key points; The curve fitting module is used to perform curve fitting on at least two sets of quadratic functions based on the key points of the first facial contour at different locations, so as to obtain at least two sets of facial contour curves.

[0019] Another objective of this application is to provide an electronic device, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions, which, when executed by the processor, implement the facial contour data processing method provided in this application.

[0020] Another objective of this application is to provide a machine-readable storage medium storing machine-executable instructions that, when executed by one or more processors, implement the facial contour data processing method provided in this application.

[0021] Compared with the prior art, this application has the following beneficial effects: This application provides a facial contour data processing method, apparatus, and electronic device. By fitting multiple first facial contour key point data into at least two sets of quadratic curves, the relatively independent facial contour key point data is converted into a curve function that can express continuous transformation. Thus, in subsequent interpolation or facial shape adjustment processes, semantic errors in the adjustment of newly added points or points can be reduced, improving the accuracy of facial shape adjustment or facial shape setting. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of facial landmark recognition; Figure 2 A schematic diagram for adjusting facial contours; Figure 3 A flowchart illustrating the steps of the facial contour data processing method provided in this application embodiment; Figure 4 A flowchart illustrating the difference process provided in an embodiment of this application; Figure 5 A schematic diagram of an electronic device provided in an embodiment of this application; Figure 6 A schematic diagram of the functional modules of the facial contour data processing device provided in the embodiments of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0027] In the description of this application, it should be noted that the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "set up," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0029] Please see Figure 1 In scenarios involving facial shape adjustment or design (such as model sculpting), key point recognition is typically performed on the facial image first to identify key facial points, including facial contour key points (such as...). Figure 1 The key points are identified from 0 to 32, and then interpolation or position mapping is performed based on these facial contour key points.

[0030] However, these identified key points are relatively independent, and their position mapping is also performed relatively independently during facial contouring. For example, please refer to... Figure 2 When performing a face-slimming operation based on key facial contour points, the key facial contour points before adjustment (such as...) are usually... Figure 2 The key points of the light-colored facial contour are mapped one by one to the adjusted key points of the facial contour (such as...). Figure 2(Medium-dark facial contour key points). In this mapping process, there is no semantic connection or constraint between the mapping actions of each facial contour key point, nor is the aesthetic appeal of the curve formed by connecting all the facial contour key points considered. Furthermore, this one-to-one mapping slimming rule may lead to facial image distortion when there is a large angle of facial rotation.

[0031] In view of this, this embodiment provides a facial contour data processing solution to solve the above problems. The solution provided in this embodiment will be described in detail below.

[0032] Please see Figure 3 , Figure 3 This embodiment provides a facial contour data processing method, which may include the following steps.

[0033] Step S110: Obtain the face image to be processed.

[0034] In this embodiment, the face image to be processed can be an image that at least shows most of the facial contours. Preferably, the face image to be processed can be a face image viewed from a frontal perspective.

[0035] Step S120: Perform key point recognition on the face image to be processed to obtain multiple first face shape contour key points.

[0036] In this embodiment, common facial key recognition technology can be used to obtain facial key points, and the first facial contour key points representing the facial contour can be extracted from them.

[0037] Step S130: Based on the key points of the first facial contour at different locations, perform curve fitting on at least two sets of quadratic functions to obtain at least two sets of facial contour curves.

[0038] The inventors discovered that the distribution of facial contour points is usually symmetrical and conforms to a quadratic function curve, but the curve trend may be different at different positions. Therefore, in this embodiment, the plurality of first facial contour key points can be fitted to at least two sets of quadratic function curves to obtain at least two sets of facial contour curves.

[0039] In this way, by fitting multiple key points of the first facial contour into at least two sets of quadratic curves, the relatively independent key points of the facial contour are transformed into curve functions that can express continuous transformations. This reduces semantic errors in the adjustment of newly added points or positions during subsequent interpolation or facial shape adjustments, thus improving the accuracy of facial shape adjustments or settings.

[0040] The inventors discovered that, in the case of facial contours, the curve of the mandible portion below the occlusal line (the extension of the lip line after closure) and the contour of the facial portion above the occlusal line usually have different curve trends. Therefore, in one possible implementation of this embodiment, in step S130, a first loss value can be determined based on the first facial contour curve using the least squares method for the first target key point that identifies the mandible portion of the face among the first facial contour key points, and a second loss value can be determined based on the second facial contour curve for the other second target key points among the first facial contour key points besides the first target key point.

[0041] Then, based on the first target key point and the second target key point, the function parameters corresponding to the first face contour curve and the second face contour curve are solved with the aim of minimizing the sum of the first loss value and the second loss value.

[0042] Specifically, in this embodiment, the first facial contour key points include points 0 to K arranged sequentially, wherein points n to m are the first target key points, and points 0 to n and points m to K are the second target key points. For example, in Figure 1 Among the facial key points shown, points numbered 8 to 24 are the first target key points that identify the lower jaw portion of the face, and points numbered 0 to 8 and 24 to 32 are the second target key points.

[0043] In step S130, for the first target key point that identifies the lower jaw portion of the face among the first facial contour key points, a first facial contour curve is constructed:

[0044]

[0045]

[0046] And for the other second target key points in the first facial contour key points besides the first target key points, a second facial contour curve is constructed:

[0047]

[0048]

[0049] Then, the first loss value can be determined according to the following formula. :

[0050] Among them, in the above formula ( , () represents the coordinate pair from the nth first facial contour key point to the mth first facial contour key point. , , Let be the function parameters to be solved for the first facial contour curve. The function parameters to be solved are common to both the first and second facial contour curves.

[0051] The second loss value can be determined according to the following formula. :

[0052] Among them, in the above formula ( , () represents the coordinate pairs from the 0th first facial contour key point to the nth first facial contour key point and from the mth first facial contour key point to the Kth first facial contour key point. , , , where is the function parameter to be solved for the second facial contour curve.

[0053] Then, based on the first loss value mentioned above... and the second loss value Calculate the sum of the two. :

[0054] Then minimize the sum value Adjust the function parameters to be solved in the first and second face contour curves to the target. , , , , , , This allows us to obtain the curve functions of the adjusted first and second facial contour curves.

[0055] It should be noted that in other implementations of this embodiment, the multiple first facial contour key points can also be fitted to two or more sets of curves. For example, the first facial contour key points can be divided into multiple groups according to multiple dividing lines such as the occlusal line, the horizontal line where the cheekbone is located, and the horizontal line where the corner of the eye is located, thereby fitting to multiple different quadratic functions.

[0056] In one possible implementation, after determining the facial contour curve, the first facial contour key points can be interpolated based on at least two sets of the facial contour curves to obtain multiple second facial contour key points.

[0057] Specifically, in one possible implementation, a first facial contour key point can be used as a reference point. Next, the tangent line of the facial contour curve corresponding to the reference point is determined at the reference point, and the intersection point of the perpendicular line on the tangent line at a predetermined distance from the reference point and the facial contour curve is determined. Then, this intersection point is determined as the second facial contour key point obtained through interpolation, and the intersection point is used as the new reference point for further interpolation.

[0058] Optionally, after determining the intersection point, it can be first detected whether the intersection point exceeds another first facial contour key point on the facial contour curve that is closest to the starting point.

[0059] If the intersection point is not exceeded, the process proceeds to determine the intersection point as the second facial contour key point and use the intersection point as the new reference point for interpolation. If the intersection point is exceeded, the other first facial contour key point is used as the new starting point and the interpolation continues using the starting point as the new reference point.

[0060] For example, please refer to Figure 4 Interpolation can be performed by following these steps.

[0061] Step S201: Take one of the key points of the original first face shape contour as the reference point, and the first face shape contour key point is the interpolation starting point.

[0062] Step S202: Calculate the slope of the facial contour curve corresponding to the reference point at the reference point, thereby determining the tangent of the facial contour curve at the reference point.

[0063] Step S203: On the extension line of the tangent, determine a point at a preset distance L from the reference point, and calculate and determine the intersection point of the perpendicular line of the tangent at that point and the facial contour curve.

[0064] Step S204: Detect whether the intersection point exceeds another first facial contour key point adjacent to the starting point of the value.

[0065] If the limit is not exceeded, proceed to step S205.

[0066] If the number of cases exceeds the limit, proceed to step S206.

[0067] Step S205: Determine the intersection point as the key point of the second face shape contour obtained by interpolation, and then proceed to step S202.

[0068] Step S206: Use another key point of the first facial contour as a new reference point, and then proceed to step S202.

[0069] In one possible implementation, after determining the facial contour curve, the facial contour curve corresponding to two adjacent first facial contour key points can be determined first, and multiple intermediate points can be determined on the straight line connecting the two first facial contour key points. Then, the intersection of the perpendicular line from the straight line connecting the two intermediate points and the facial contour curve is determined as the second facial contour key point obtained by interpolation.

[0070] Based on the above design, compared with the existing technology that performs interpolation on the straight line connecting two key points, the solution provided in this embodiment combines the interpolation process with the facial contour curve, which makes the interpolated points more consistent with the trend of facial contour changes.

[0071] In one possible implementation, after determining the facial contour curve, at least two sets of parameters in the functions corresponding to the facial contour curve can be adjusted to obtain the adjusted facial contour curve. Then, facial image adjustment processing is performed on the face image to be processed based on the adjusted facial contour curve. For example, in the aforementioned constructed quadratic function, , The absolute value of the coefficients controls the size of the curve opening. By adjusting these coefficients, a new facial contour curve can be quickly constructed, and the image can be warped based on the adjusted facial contour curve.

[0072] This application also provides an electronic device, which can be a device with certain digital image processing capabilities. For example, the device may include a service area, personal computer, laptop computer, workstation, etc. Please refer to... Figure 5 , Figure 5 This is a block diagram of the electronic device 100. The electronic device 100 includes a facial contour data processing device 110, a machine-readable storage medium 120, and a processor 130.

[0073] The machine-readable storage medium 120 and the processor 130 are electrically connected directly or indirectly to each other to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The facial contour data processing device 110 includes at least one software function module that can be stored in the machine-readable storage medium 120 in the form of software or firmware or embedded in the operating system (OS) of the electronic device 100. The processor 130 is used to execute the executable modules stored in the machine-readable storage medium 120, such as the software function modules and computer programs included in the facial contour data processing device 110.

[0074] The machine-readable storage medium 120 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The machine-readable storage medium 120 is used to store programs, and the processor 130 executes these programs / executable the facial contour data processing method provided in this embodiment after receiving execution instructions.

[0075] The processor 130 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor.

[0076] Please refer to Figure 6This embodiment also provides a facial contour data processing device 110, which includes at least one functional module that can be stored in a machine-readable storage medium 120 in software form. Functionally, the facial contour data processing device 110 may include a data acquisition module 111, a key point recognition module 112, and a curve fitting module 113.

[0077] The data acquisition module 111 is used to acquire the face image to be processed.

[0078] In this embodiment, the data acquisition module 111 can be used to perform... Figure 3 For a detailed description of the data acquisition module 111 shown in step S110, please refer to the description of step S110.

[0079] The key point recognition module 112 is used to perform key point recognition on the face image to be processed to obtain multiple first face shape contour key points.

[0080] In this embodiment, the key point recognition module 112 can be used to perform... Figure 3 For a detailed description of the key point recognition module 112 shown in step S120, please refer to the description of step S120.

[0081] The curve fitting module 113 is used to perform curve fitting processing on at least two sets of quadratic functions based on the first facial contour key points at different locations, so as to obtain at least two sets of facial contour curves.

[0082] In this embodiment, the curve fitting module 113 can be used to perform... Figure 3 For a detailed description of the curve fitting module 113 shown in step S130, please refer to the description of step S130.

[0083] In summary, the embodiments of this application provide a facial contour data processing method, apparatus, and electronic device. By fitting multiple first facial contour key point data into at least two sets of quadratic curves, the facial contour key point data of relatively independent points are converted into curve functions that can express continuous transformations. Thus, in subsequent interpolation or facial shape adjustment processes, semantic errors in the adjustment actions of newly added points or points during interpolation can be reduced, improving the accuracy of facial shape adjustment or facial shape setting.

[0084] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0085] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0086] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) 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.

[0087] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0088] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for processing facial contour data, characterized in that, The method includes: Obtain the face image to be processed; Key point recognition is performed on the face image to be processed to obtain multiple first face shape contour key points; For the first target key point that identifies the lower jaw portion of the face in the first facial contour key point, the first loss value is determined by the least squares method based on the first facial contour curve; wherein, the lower jaw portion is the part below the extension line after the lips are closed in the face image to be processed; For the second target key points other than the first target key points in the first face shape contour key points, a second loss value is determined based on the second face shape contour curve; Based on the first target key point and the second target key point, the parameters of the quadratic functions corresponding to the first face contour curve and the second face contour curve are solved with the aim of minimizing the sum of the first loss value and the second loss value.

2. The method according to claim 1, characterized in that, The method further includes: Based on the first face contour curve and the second face contour curve, interpolation processing is performed on the key points of the first face contour to obtain multiple key points of the second face contour.

3. The method according to claim 2, characterized in that, The step of interpolating the key points of the first facial contour based on the first facial contour curve and the second facial contour curve includes: Use the first facial contour key point as the reference point; Determine the tangent line of the first facial contour curve or the second facial contour curve corresponding to the reference point at the reference point; Determine the intersection point of the perpendicular line on the tangent line at a preset distance from the reference point and the first face shape contour curve or the second face shape contour curve; The intersection point is determined as the key point of the second facial contour obtained by interpolation, and the intersection point is used as the new reference point for further interpolation.

4. The method according to claim 2, characterized in that, The step of interpolating the key points of the first facial contour based on the first facial contour curve and the second facial contour curve includes: Determine the first face contour curve or the second face contour curve corresponding to two adjacent first face contour key points, and determine multiple intermediate points on the straight line connecting the two first face contour key points. The intersection of the perpendicular line drawn from the straight line at each of the intermediate points with the first or second face contour curve is determined as the key point of the second face contour obtained by interpolation.

5. The method according to claim 1, characterized in that, The method further includes: Adjust the parameters in the functions corresponding to the first and second face contour curves to obtain the adjusted first and second face contour curves. Based on the adjusted first facial contour curve and the second facial contour curve, the facial image to be processed is determined to undergo facial image adjustment processing.

6. A facial contour data processing device, characterized in that, The facial contour data processing device includes: The data acquisition module is used to acquire the face image to be processed; The key point recognition module is used to perform key point recognition on the face image to be processed and obtain multiple first face shape contour key points; The curve fitting module determines a first loss value based on the first face contour curve using the least squares method for the first target key point that identifies the lower jaw portion of the face among the first face contour key points; wherein, the lower jaw portion is the part below the extension line after the lips are closed in the face image to be processed; for other second target key points in the first face contour key points besides the first target key point, a second loss value is determined based on the second face contour curve; and based on the first target key point and the second target key point, the parameters of the quadratic functions corresponding to the first face contour curve and the second face contour curve are solved with the aim of minimizing the sum of the first loss value and the second loss value.

7. An electronic device, characterized in that, The method includes a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions, which, when executed by the processor, implement the method according to any one of claims 1-5.

8. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores machine-executable instructions that, when executed by one or more processors, implement the method according to any one of claims 1-5.

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